In the signal processing for metrewave radar, the reflection paths of target echoes can cause severe error in the elevation estimation for the low-angle target tracking. The exact angles of the reflection paths are un...In the signal processing for metrewave radar, the reflection paths of target echoes can cause severe error in the elevation estimation for the low-angle target tracking. The exact angles of the reflection paths are unknown beforehand, and therefore, the reflection paths can not be suppressed easily. Therefore, in this article, an improved reflection paths suppression approach is presented. A block matrix aggregate is constructed based on the possible angles of the reflection paths. Combined with the beamforming-like processing, a generalized maximum likelihood estimation is derived to optimize the estimation. Moreover, the noise reduction method based on the Toeplitz covariance matrix is used for better performance. This approach is applied to the real data collected by the low-angle tracking radar with 8-channel vertical array. The experiment results show that the reflection effects are reduced and the accuracy of the elevation estimate is improved.展开更多
In this article, robust generalized estimating equation for the analysis of partial linear mixed model for longitudinal data is used. The authors approximate the nonparametric function by a regression spline. Under so...In this article, robust generalized estimating equation for the analysis of partial linear mixed model for longitudinal data is used. The authors approximate the nonparametric function by a regression spline. Under some regular conditions, the asymptotic properties of the estimators are obtained. To avoid the computation of high-dimensional integral, a robust Monte Carlo Newton-Raphson algorithm is used. Some simulations are carried out to study the performance of the proposed robust estimators. In addition, the authors also study the robustness and the efficiency of the proposed estimators by simulation. Finally, two real longitudinal data sets are analyzed.展开更多
In this paper we give a priori estimates for the maximum modulus of generalizedsolulions of the quasilinear elliplic equations irith anisotropic growth condition.
Objective:To explore the appropriate modeling method of the early warning model of ischemic stroke recurrence in TCM.Methods:This was a prospective,multi-center and registered study conducted in 7 clinical subcenters ...Objective:To explore the appropriate modeling method of the early warning model of ischemic stroke recurrence in TCM.Methods:This was a prospective,multi-center and registered study conducted in 7 clinical subcenters from 8 provinces and 10 cities in China between 3rd November 2016 and 27th April,2019.1,741 patients with first-ever ischemic stroke were recruited.Univariate analysis was carried out using distance correlation coefficient,mutual information entropy,and statistical correlation test.Multivariate analysis adopted multi-factor Cox regression model and combined with expert opinions in the field of stroke to determine modeling variables.The generalized estimating equation of longitudinal data and the Cox proportional hazard regression model of cross-sectional data were used to construct and compare in the early warning model of ischemic stroke recalls.The area under the ROC curve(AUC value)was used to evaluate the early warning capability of the model.Results:The follow-up time was 1-3 years,and the median follow-up time was 1.42 years(95%CI:1.37-1.47).Recurrence events occurred in 175 cases,and the cumulative recurrence rate was 10.05%(95%CI:8.64%-11.47%).The AUC values of the TCM syndrome and TCM constitution model were 0.71809 and 0.72668 based on the generalized estimating equation and the AUC values.Conclusion:The generalized estimating equation may be more suitable for the construction of early warning models of stroke recurrence with TCM characteristics,which provides a certain reference for the evaluation of secondary prevention of ischemic stroke.展开更多
When multicollinearity is present in a set of the regression variables, the least square estimate of the regression coefficient tends to be unstable and it may lead to erroneous inference.In this paper, generalized ri...When multicollinearity is present in a set of the regression variables, the least square estimate of the regression coefficient tends to be unstable and it may lead to erroneous inference.In this paper, generalized ridge estimate (K) of the regression coefficient = vec(B) is considered in multivaiale linear regression model. The MSE of the above estimate is less than the MSE of the least square estimate by choosing the ridge parameter matrix K. Moreover, it is pointed out that the Criterion MSE for choosing matrix K of generalized ridge estimate has several weaknesses. In order to overcome these weaknesses, a new family of criteria Q(c) is adpoted which includes the criterion MSE and criterion LS as its special case. The good properties of the criteria Q(c) are proved and discussed from theoretical point of view. The statistical meaning of the scale c is explained and the methods of determining c are also given.展开更多
In many fields, we need to deal with hierarchically structured data.For this kind of data, hierarchical mixed effects model can show the correlationof variables in the same level by establishing a model for regression...In many fields, we need to deal with hierarchically structured data.For this kind of data, hierarchical mixed effects model can show the correlationof variables in the same level by establishing a model for regression coefficients.Due to the complexity of the random part in this model, seeking an effectivemethod to estimate the covariance matrix is an appealing issue. Iterative generalizedleast squares estimation method was proposed by Goldstein in 1986 and wasapplied in special case of hierarchical model. In this paper, we extend themethod to the general hierarchical mixed effects model, derive its expressions indetail and apply it to economic examples.展开更多
Objective This study explored the correlation of longitudinal changes in serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels with the incidence of metabolic syndrome (Mets) based on ...Objective This study explored the correlation of longitudinal changes in serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels with the incidence of metabolic syndrome (Mets) based on a dynamic health examination cohort. Methods A Mets-free dynamic cohort involving 4541 participants who underwent at least three health examinations from 2006 to 2011 was included in the study. Mets was defined according to the Chinese Medical Association Diabetes Branch definition that included hypertension, obesity, hyperlipidemia, and hyperglycemia. Generalized estimating equation (GEE) model was used to analyze multivariate relative risk (RR) of repeated observations of ALT and AST in quartiles for Mets or its components according to gender. Results In all, 826 Mets cases were reported. Adjustment of relevant parameters indicated that time-varying changes in ALT and AST levels were positively associated with the incidence of Mets in a dose-response manner. Positive association between high ALT levels and fatty liver was much stronger than that between high AST levels and fatty liver, particularly in male participants. These associations were consistently observed in the following subgroups: participants with ALT and AST levels of 〈40 U/L, participants with of 〈25 kg/m2, and participants with non-fatty liver. Furthermore, participants with 2 Mets components at baseline showed lower multivariate adjusted RRs of ALT and AST for Mets than participants with 0-1 Mets component. Conclusion These results suggested that elevated serum ALT and AST levels were early biomarkers of Mets or its components.展开更多
BACKGROUND Genome-wide association studies from Asia indicate that HLA-DP and HLA-DQ loci are important in persistent hepatitis B virus(HBV)infections.One of the key elements for HBV-related carcinogenesis is persiste...BACKGROUND Genome-wide association studies from Asia indicate that HLA-DP and HLA-DQ loci are important in persistent hepatitis B virus(HBV)infections.One of the key elements for HBV-related carcinogenesis is persistent viral replication and inflammation.AIM To examine genetic and nongenetic factors with persistent HBV infection and viral load in families with hepatocellular carcinoma(HCC).METHODS The HCC families included 301 hepatitis B surface antigen(HBsAg)carriers and 424 noncarriers born before the nationwide vaccination program was initiated in 1984.Five HBV-related single nucleotide polymorphisms(SNPs)—rs477515,rs9272105,rs9276370,rs7756516,and rs9277535—were genotyped.Factors associated with persistent HBV infection and viral load were analyzed by a generalized estimating equation.RESULTS In the first-stage persistent HBV study,all SNPs except rs9272105 were associated with persistent infection.A significantly higher area under the reciprocal operating characteristic curve for nongenetic factors vs genetic factors(P<0.001)suggests that the former play a major role in persistent HBV infection.In the second-stage viral load study,we added 8 HBsAg carriers born after 1984.The 309 HBsAg carriers were divided into low(n=162)and high viral load(n=147)groups with an HBV DNA cutoff of 105 cps/mL.Sex,relationship to the index case,rs477515,rs9272105,and rs7756516 were associated with viral load.Based on the receiver operating characteristic curve analysis,genetic and nongenetic factors affected viral load equally in the HCC family cohort(P=0.3117).CONCLUSION In these east Asian adults,the mechanism of persistent HBV infection-related SNPs was a prolonged viral replication phase.展开更多
In longitudinal data analysis, our primary interest is in the estimation of regression parameters for the marginal expectations of the longitudinal responses, and the longitudinal correlation parameters are of seconda...In longitudinal data analysis, our primary interest is in the estimation of regression parameters for the marginal expectations of the longitudinal responses, and the longitudinal correlation parameters are of secondary interest. The joint likelihood function for longitudinal data is challenging, particularly due to correlated responses. Marginal models, such as generalized estimating equations (GEEs), have received much attention based on the assumption of the first two moments of the data and a working correlation structure. The confidence regions and hypothesis tests are constructed based on the asymptotic normality. This approach is sensitive to the misspecification of the variance function and the working correlation structure which may yield inefficient and inconsistent estimates leading to wrong conclusions. To overcome this problem, we propose an empirical likelihood (EL) procedure based on a set of estimating equations for the parameter of interest and discuss its <span style="font-family:Verdana;">characteristics and asymptotic properties. We also provide an algorithm base</span><span style="font-family:Verdana;">d on EL principles for the estimation of the regression parameters and the construction of its confidence region. We have applied the proposed method in two case examples.</span>展开更多
The purpose of this article is to investigate approaches for modeling individual patient count/rate data over time accounting for temporal correlation and non</span><span style="font-family:Verdana;"...The purpose of this article is to investigate approaches for modeling individual patient count/rate data over time accounting for temporal correlation and non</span><span style="font-family:Verdana;">-</span><span style="font-family:Verdana;">constant dispersions while requiring reasonable amounts of time to search over alternative models for those data. This research addresses formulations for two approaches for extending generalized estimating equations (GEE) modeling. These approaches use a likelihood-like function based on the multivariate normal density. The first approach augments standard GEE equations to include equations for estimation of dispersion parameters. The second approach is based on estimating equations determined by partial derivatives of the likelihood-like function with respect to all model parameters and so extends linear mixed modeling. Three correlation structures are considered including independent, exchangeable, and spatial autoregressive of order 1 correlations. The likelihood-like function is used to formulate a likelihood-like cross-validation (LCV) score for use in evaluating models. Example analyses are presented using these two modeling approaches applied to three data sets of counts/rates over time for individual cancer patients including pain flares per day, as needed pain medications taken per day, and around the clock pain medications taken per day per dose. Means and dispersions are modeled as possibly nonlinear functions of time using adaptive regression modeling methods to search through alternative models compared using LCV scores. The results of these analyses demonstrate that extended linear mixed modeling is preferable for modeling individual patient count/rate data over time</span><span style="font-family:Verdana;">,</span><span style="font-family:Verdana;"> because in example analyses</span><span style="font-family:Verdana;">,</span><span style="font-family:Verdana;"> it either generates better LCV scores or more parsimonious models and requires substantially less time.展开更多
The study analyses the theoretical mechanism through which environmental regulation affects the dairy industry’s technological progress,with a particular focus on how the effect is conditional on farm size.Using the ...The study analyses the theoretical mechanism through which environmental regulation affects the dairy industry’s technological progress,with a particular focus on how the effect is conditional on farm size.Using the input–output data of dairy farms of different sizes from 2009 to 2019 in 10 Chinese provinces/autonomous regions in China and the quantitative measurement index of environmental regulation,the study estimates environmental regulation’s heterogeneous influences on the dairy industry’s technological progress by dynamic panel data models.The empirical results suggest that,first,environmental regulation has a U-type influence on the technological progress of dairy farming.The U-type influence means moving from pollution control’s high cost and low technology progress to the high profit and high innovation input generated by optimizing the breeding structure.Second,the promotion of dairy farming technology depends on farm size.The effect of environmental regulation on technological progress in moderately large-farms showed a U-type relationship.In contrast,the effect in free-range and large-size dairy farms showed a linear and positive relationship.The government should further strengthen environmental regulation based on advancing moderately large-farms in compliance with market mechanisms in the long run.Particular attention should be paid to the forms of environmental regulation so that dairy cattle breeding technology can break through the inflection point of the“U”curve as soon as possible and ensure the significance of the rising stage.Along the way,technical support should be provided for realizing environmental protection and economic growth.展开更多
We describe the design of a multiple maneuvering targets tracking algorithm under the framework of Gaussian mixture probability hypothesis density(PHD) filter.First,a variation of the generalized pseudo-Bayesian estim...We describe the design of a multiple maneuvering targets tracking algorithm under the framework of Gaussian mixture probability hypothesis density(PHD) filter.First,a variation of the generalized pseudo-Bayesian estimator of first order(VGPB1) is designed to adapt to the Gaussian mixture PHD filter for jump Markov system models(JMS-PHD).The probability of each kinematic model,which is used in the JMS-PHD filter,is updated with VGPB1.The weighted sum of state,associated covariance,and weights for Gaussian components are then calculated.Pruning and merging techniques are also adopted in this algorithm to increase efficiency.Performance of the proposed algorithm is compared with that of the JMS-PHD filter.Monte-Carlo simulation results demonstrate that the optimal subpattern assignment(OSPA) distances of the proposed algorithm are lower than those of the JMS-PHD filter for maneuvering targets tracking.展开更多
In terms of the degree-of-freedom of bank loan decision-making, the ratio of loans of private enterprises and individuals to total loans is used to measure the development of China 's financial intermediation. Applyi...In terms of the degree-of-freedom of bank loan decision-making, the ratio of loans of private enterprises and individuals to total loans is used to measure the development of China 's financial intermediation. Applying generalized method of moments estimation developed for dynamic panel data models, the present paper finds that the effect of financial intermediation development on economic growth is positive and statistieally significant when controlling for other variables, such as human capital foreign direct investment, securitization and foreign trade. The empirical results indicate that the concept of the so-called Chinese counterexample in financial development is questionable. Financial system reforms, including encouraging banks to operate independently, reducing or eliminating mandatory loans, and maldngfinancial decision-making more market-oriented, are important for China's economic growth.展开更多
The method of generalized estimating equations (GEE) introduced by K. Y. Liang and S. L. Zeger has been widely used to analyze longitudinal data. Recently, this method has been criticized for a failure to protect ag...The method of generalized estimating equations (GEE) introduced by K. Y. Liang and S. L. Zeger has been widely used to analyze longitudinal data. Recently, this method has been criticized for a failure to protect against misspecification of working correlation models, which in some cases leads to loss of efficiency or infeasibility of solutions. In this paper, we present a new method named as 'weighted estimating equations (WEE)' for estimating the correlation parameters. The new estimates of correlation parameters are obtained as the solutions of these weighted estimating equations. For some commonly assumed correlation structures, we show that there exists a unique feasible solution to these weighted estimating equations regardless the correlation structure is correctly specified or not. The new feasible estimates of correlation parameters are consistent when the working correlation structure is correctly specified. Simulation results suggest that the new method works well in finite samples.展开更多
Multivariate failure time data are frequently encountered in biomedical research.In this article,we model marginal hazards with accelerated hazards model to analyze multivariate failure time data.Estimating equations ...Multivariate failure time data are frequently encountered in biomedical research.In this article,we model marginal hazards with accelerated hazards model to analyze multivariate failure time data.Estimating equations are derived analogous to generalized estimating equation method.Under certain regular conditions,the resultant estimators for the regression parameters are shown to be asymptotically normal.Furthermore,we also establish the weak convergence of estimators for the baseline cumulative hazard functions.展开更多
Consider a partially linear regression model with an unknown vector parameter , an unknown function g(·), and unknown heteroscedastic error variances. Chen, You<SUP>[23]</SUP> proposed a semiparametri...Consider a partially linear regression model with an unknown vector parameter , an unknown function g(·), and unknown heteroscedastic error variances. Chen, You<SUP>[23]</SUP> proposed a semiparametric generalized least squares estimator (SGLSE) for , which takes the heteroscedasticity into account to increase efficiency. For inference based on this SGLSE, it is necessary to construct a consistent estimator for its asymptotic covariance matrix. However, when there exists within-group correlation, the traditional delta method and the delete-1 jackknife estimation fail to offer such a consistent estimator. In this paper, by deleting grouped partial residuals a delete-group jackknife method is examined. It is shown that the delete-group jackknife method indeed can provide a consistent estimator for the asymptotic covariance matrix in the presence of within-group correlations. This result is an extension of that in [21].展开更多
Based on the generalized estimating equation approach,the authors propose a parsimonious mean-covariance model for longitudinal data with autoregressive and moving average error process,which not only unites the exist...Based on the generalized estimating equation approach,the authors propose a parsimonious mean-covariance model for longitudinal data with autoregressive and moving average error process,which not only unites the existing autoregressive Cholesky factor model and moving average Cholesky factor model but also provides a wide variety of structures of covariance matrix.The resulting estimators for the regression coefficients in both the mean and the covariance are shown to be consistent and asymptotically normally distributed under mild conditions.The authors demonstrate the effectiveness,parsimoniousness and desirable performance of the proposed approach by analyzing the CD4-I-cell counts data set and conducting extensive simulations.展开更多
In this paper, we introduce a generalized Liu estimator and jackknifed Liu estimator in a linear regression model with correlated or heteroscedastic errors. Therefore, we extend the Liu estimator. Under the mean squar...In this paper, we introduce a generalized Liu estimator and jackknifed Liu estimator in a linear regression model with correlated or heteroscedastic errors. Therefore, we extend the Liu estimator. Under the mean square error(MSE), the jackknifed estimator is superior to the Liu estimator and the jackknifed ridge estimator. We also give a method to select the biasing parameter for d. Furthermore, a numerical example is given to illustvate these theoretical results.展开更多
This paper studies estimation and serial correlation test of a semiparametric varying-coefficient partially linear EV model of the form Y = X^Tβ +Z^Tα(T) +ε,ξ = X + η with the identifying condition E[(ε,...This paper studies estimation and serial correlation test of a semiparametric varying-coefficient partially linear EV model of the form Y = X^Tβ +Z^Tα(T) +ε,ξ = X + η with the identifying condition E[(ε,η^T)^T] =0, Cov[(ε,η^T)^T] = σ^2Ip+1. The estimators of interested regression parameters /3 , and the model error variance σ2, as well as the nonparametric components α(T), are constructed. Under some regular conditions, we show that the estimators of the unknown vector β and the unknown parameter σ2 are strongly consistent and asymptotically normal and that the estimator of α(T) achieves the optimal strong convergence rate of the usual nonparametric regression. Based on these estimators and asymptotic properties, we propose the VN,p test statistic and empirical log-likelihood ratio statistic for testing serial correlation in the model. The proposed statistics are shown to have asymptotic normal or chi-square distributions under the null hypothesis of no serial correlation. Some simulation studies are conducted to illustrate the finite sample performance of the proposed tests.展开更多
Let Y have an n-variate normal distribution with covariance matrim σ2I and mean vector Xβ,where X is a known n×p matrix. The problem of estimating θ=σ2+β′X′CXβ is studied. The admissibility and inadmissib...Let Y have an n-variate normal distribution with covariance matrim σ2I and mean vector Xβ,where X is a known n×p matrix. The problem of estimating θ=σ2+β′X′CXβ is studied. The admissibility and inadmissibility of the estimators of the form bS2+β′X′CXβ, where β= (X′X) ˉX′Y and S2=(Y-Xβ)′(Y-Xβ), are established. Another class of admissible quadratic estimators of θ is derived.展开更多
文摘In the signal processing for metrewave radar, the reflection paths of target echoes can cause severe error in the elevation estimation for the low-angle target tracking. The exact angles of the reflection paths are unknown beforehand, and therefore, the reflection paths can not be suppressed easily. Therefore, in this article, an improved reflection paths suppression approach is presented. A block matrix aggregate is constructed based on the possible angles of the reflection paths. Combined with the beamforming-like processing, a generalized maximum likelihood estimation is derived to optimize the estimation. Moreover, the noise reduction method based on the Toeplitz covariance matrix is used for better performance. This approach is applied to the real data collected by the low-angle tracking radar with 8-channel vertical array. The experiment results show that the reflection effects are reduced and the accuracy of the elevation estimate is improved.
基金the Natural Science Foundation of China(10371042,10671038)
文摘In this article, robust generalized estimating equation for the analysis of partial linear mixed model for longitudinal data is used. The authors approximate the nonparametric function by a regression spline. Under some regular conditions, the asymptotic properties of the estimators are obtained. To avoid the computation of high-dimensional integral, a robust Monte Carlo Newton-Raphson algorithm is used. Some simulations are carried out to study the performance of the proposed robust estimators. In addition, the authors also study the robustness and the efficiency of the proposed estimators by simulation. Finally, two real longitudinal data sets are analyzed.
文摘In this paper we give a priori estimates for the maximum modulus of generalizedsolulions of the quasilinear elliplic equations irith anisotropic growth condition.
基金National Key R&D Program of the Ministry of Science and TechnologyConstruction of the Technical System for"Treating the Disease"in Traditional Chinese Medicine(No.2018YFC1704705)2015 Special Research Project of the Chinese Medicine Industry of the National Administration of Traditional Chinese Medicine:R&D and Demonstration of Recurrence Risk Assessment System for Ischemic Stroke Disease with Chinese Medicine Characteristic Health Management(No.201507003-8).
文摘Objective:To explore the appropriate modeling method of the early warning model of ischemic stroke recurrence in TCM.Methods:This was a prospective,multi-center and registered study conducted in 7 clinical subcenters from 8 provinces and 10 cities in China between 3rd November 2016 and 27th April,2019.1,741 patients with first-ever ischemic stroke were recruited.Univariate analysis was carried out using distance correlation coefficient,mutual information entropy,and statistical correlation test.Multivariate analysis adopted multi-factor Cox regression model and combined with expert opinions in the field of stroke to determine modeling variables.The generalized estimating equation of longitudinal data and the Cox proportional hazard regression model of cross-sectional data were used to construct and compare in the early warning model of ischemic stroke recalls.The area under the ROC curve(AUC value)was used to evaluate the early warning capability of the model.Results:The follow-up time was 1-3 years,and the median follow-up time was 1.42 years(95%CI:1.37-1.47).Recurrence events occurred in 175 cases,and the cumulative recurrence rate was 10.05%(95%CI:8.64%-11.47%).The AUC values of the TCM syndrome and TCM constitution model were 0.71809 and 0.72668 based on the generalized estimating equation and the AUC values.Conclusion:The generalized estimating equation may be more suitable for the construction of early warning models of stroke recurrence with TCM characteristics,which provides a certain reference for the evaluation of secondary prevention of ischemic stroke.
基金The projects Supported by Natural Science Foundation of Fujian Province
文摘When multicollinearity is present in a set of the regression variables, the least square estimate of the regression coefficient tends to be unstable and it may lead to erroneous inference.In this paper, generalized ridge estimate (K) of the regression coefficient = vec(B) is considered in multivaiale linear regression model. The MSE of the above estimate is less than the MSE of the least square estimate by choosing the ridge parameter matrix K. Moreover, it is pointed out that the Criterion MSE for choosing matrix K of generalized ridge estimate has several weaknesses. In order to overcome these weaknesses, a new family of criteria Q(c) is adpoted which includes the criterion MSE and criterion LS as its special case. The good properties of the criteria Q(c) are proved and discussed from theoretical point of view. The statistical meaning of the scale c is explained and the methods of determining c are also given.
文摘In many fields, we need to deal with hierarchically structured data.For this kind of data, hierarchical mixed effects model can show the correlationof variables in the same level by establishing a model for regression coefficients.Due to the complexity of the random part in this model, seeking an effectivemethod to estimate the covariance matrix is an appealing issue. Iterative generalizedleast squares estimation method was proposed by Goldstein in 1986 and wasapplied in special case of hierarchical model. In this paper, we extend themethod to the general hierarchical mixed effects model, derive its expressions indetail and apply it to economic examples.
文摘Objective This study explored the correlation of longitudinal changes in serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels with the incidence of metabolic syndrome (Mets) based on a dynamic health examination cohort. Methods A Mets-free dynamic cohort involving 4541 participants who underwent at least three health examinations from 2006 to 2011 was included in the study. Mets was defined according to the Chinese Medical Association Diabetes Branch definition that included hypertension, obesity, hyperlipidemia, and hyperglycemia. Generalized estimating equation (GEE) model was used to analyze multivariate relative risk (RR) of repeated observations of ALT and AST in quartiles for Mets or its components according to gender. Results In all, 826 Mets cases were reported. Adjustment of relevant parameters indicated that time-varying changes in ALT and AST levels were positively associated with the incidence of Mets in a dose-response manner. Positive association between high ALT levels and fatty liver was much stronger than that between high AST levels and fatty liver, particularly in male participants. These associations were consistently observed in the following subgroups: participants with ALT and AST levels of 〈40 U/L, participants with of 〈25 kg/m2, and participants with non-fatty liver. Furthermore, participants with 2 Mets components at baseline showed lower multivariate adjusted RRs of ALT and AST for Mets than participants with 0-1 Mets component. Conclusion These results suggested that elevated serum ALT and AST levels were early biomarkers of Mets or its components.
基金Supported by Chang Gung Memorial Hospital,No.CMRPG3C0701and National Science Council,No.NSC101-2314-B-182A-025-MY3 and No.MOST 107-2314-B-039-059.
文摘BACKGROUND Genome-wide association studies from Asia indicate that HLA-DP and HLA-DQ loci are important in persistent hepatitis B virus(HBV)infections.One of the key elements for HBV-related carcinogenesis is persistent viral replication and inflammation.AIM To examine genetic and nongenetic factors with persistent HBV infection and viral load in families with hepatocellular carcinoma(HCC).METHODS The HCC families included 301 hepatitis B surface antigen(HBsAg)carriers and 424 noncarriers born before the nationwide vaccination program was initiated in 1984.Five HBV-related single nucleotide polymorphisms(SNPs)—rs477515,rs9272105,rs9276370,rs7756516,and rs9277535—were genotyped.Factors associated with persistent HBV infection and viral load were analyzed by a generalized estimating equation.RESULTS In the first-stage persistent HBV study,all SNPs except rs9272105 were associated with persistent infection.A significantly higher area under the reciprocal operating characteristic curve for nongenetic factors vs genetic factors(P<0.001)suggests that the former play a major role in persistent HBV infection.In the second-stage viral load study,we added 8 HBsAg carriers born after 1984.The 309 HBsAg carriers were divided into low(n=162)and high viral load(n=147)groups with an HBV DNA cutoff of 105 cps/mL.Sex,relationship to the index case,rs477515,rs9272105,and rs7756516 were associated with viral load.Based on the receiver operating characteristic curve analysis,genetic and nongenetic factors affected viral load equally in the HCC family cohort(P=0.3117).CONCLUSION In these east Asian adults,the mechanism of persistent HBV infection-related SNPs was a prolonged viral replication phase.
文摘In longitudinal data analysis, our primary interest is in the estimation of regression parameters for the marginal expectations of the longitudinal responses, and the longitudinal correlation parameters are of secondary interest. The joint likelihood function for longitudinal data is challenging, particularly due to correlated responses. Marginal models, such as generalized estimating equations (GEEs), have received much attention based on the assumption of the first two moments of the data and a working correlation structure. The confidence regions and hypothesis tests are constructed based on the asymptotic normality. This approach is sensitive to the misspecification of the variance function and the working correlation structure which may yield inefficient and inconsistent estimates leading to wrong conclusions. To overcome this problem, we propose an empirical likelihood (EL) procedure based on a set of estimating equations for the parameter of interest and discuss its <span style="font-family:Verdana;">characteristics and asymptotic properties. We also provide an algorithm base</span><span style="font-family:Verdana;">d on EL principles for the estimation of the regression parameters and the construction of its confidence region. We have applied the proposed method in two case examples.</span>
文摘The purpose of this article is to investigate approaches for modeling individual patient count/rate data over time accounting for temporal correlation and non</span><span style="font-family:Verdana;">-</span><span style="font-family:Verdana;">constant dispersions while requiring reasonable amounts of time to search over alternative models for those data. This research addresses formulations for two approaches for extending generalized estimating equations (GEE) modeling. These approaches use a likelihood-like function based on the multivariate normal density. The first approach augments standard GEE equations to include equations for estimation of dispersion parameters. The second approach is based on estimating equations determined by partial derivatives of the likelihood-like function with respect to all model parameters and so extends linear mixed modeling. Three correlation structures are considered including independent, exchangeable, and spatial autoregressive of order 1 correlations. The likelihood-like function is used to formulate a likelihood-like cross-validation (LCV) score for use in evaluating models. Example analyses are presented using these two modeling approaches applied to three data sets of counts/rates over time for individual cancer patients including pain flares per day, as needed pain medications taken per day, and around the clock pain medications taken per day per dose. Means and dispersions are modeled as possibly nonlinear functions of time using adaptive regression modeling methods to search through alternative models compared using LCV scores. The results of these analyses demonstrate that extended linear mixed modeling is preferable for modeling individual patient count/rate data over time</span><span style="font-family:Verdana;">,</span><span style="font-family:Verdana;"> because in example analyses</span><span style="font-family:Verdana;">,</span><span style="font-family:Verdana;"> it either generates better LCV scores or more parsimonious models and requires substantially less time.
基金supported by the Ministry of Agriculture and Rural Affairs,China(125D0301)。
文摘The study analyses the theoretical mechanism through which environmental regulation affects the dairy industry’s technological progress,with a particular focus on how the effect is conditional on farm size.Using the input–output data of dairy farms of different sizes from 2009 to 2019 in 10 Chinese provinces/autonomous regions in China and the quantitative measurement index of environmental regulation,the study estimates environmental regulation’s heterogeneous influences on the dairy industry’s technological progress by dynamic panel data models.The empirical results suggest that,first,environmental regulation has a U-type influence on the technological progress of dairy farming.The U-type influence means moving from pollution control’s high cost and low technology progress to the high profit and high innovation input generated by optimizing the breeding structure.Second,the promotion of dairy farming technology depends on farm size.The effect of environmental regulation on technological progress in moderately large-farms showed a U-type relationship.In contrast,the effect in free-range and large-size dairy farms showed a linear and positive relationship.The government should further strengthen environmental regulation based on advancing moderately large-farms in compliance with market mechanisms in the long run.Particular attention should be paid to the forms of environmental regulation so that dairy cattle breeding technology can break through the inflection point of the“U”curve as soon as possible and ensure the significance of the rising stage.Along the way,technical support should be provided for realizing environmental protection and economic growth.
基金Project supported by the National Natural Science Foundation of China(Nos.61175008,60935001,and 61104210)the Aviation Foundation(No.20112057005)the National Basic Research Program(973) of China(No.2009CB824900)
文摘We describe the design of a multiple maneuvering targets tracking algorithm under the framework of Gaussian mixture probability hypothesis density(PHD) filter.First,a variation of the generalized pseudo-Bayesian estimator of first order(VGPB1) is designed to adapt to the Gaussian mixture PHD filter for jump Markov system models(JMS-PHD).The probability of each kinematic model,which is used in the JMS-PHD filter,is updated with VGPB1.The weighted sum of state,associated covariance,and weights for Gaussian components are then calculated.Pruning and merging techniques are also adopted in this algorithm to increase efficiency.Performance of the proposed algorithm is compared with that of the JMS-PHD filter.Monte-Carlo simulation results demonstrate that the optimal subpattern assignment(OSPA) distances of the proposed algorithm are lower than those of the JMS-PHD filter for maneuvering targets tracking.
基金supported by the Social and Humanity Sciences Foundation of the Ministry of Education of the People's Republic of China for young people (No.09YJC790243)the National Natural Science Foundation of China (No. 70773103)the Project of Key Research Base of Human and Social Sciences (Finance) for Colleges in Zhejiang Province (Grant Number of Academic Education of Zhejiang [2008]255)
文摘In terms of the degree-of-freedom of bank loan decision-making, the ratio of loans of private enterprises and individuals to total loans is used to measure the development of China 's financial intermediation. Applying generalized method of moments estimation developed for dynamic panel data models, the present paper finds that the effect of financial intermediation development on economic growth is positive and statistieally significant when controlling for other variables, such as human capital foreign direct investment, securitization and foreign trade. The empirical results indicate that the concept of the so-called Chinese counterexample in financial development is questionable. Financial system reforms, including encouraging banks to operate independently, reducing or eliminating mandatory loans, and maldngfinancial decision-making more market-oriented, are important for China's economic growth.
文摘The method of generalized estimating equations (GEE) introduced by K. Y. Liang and S. L. Zeger has been widely used to analyze longitudinal data. Recently, this method has been criticized for a failure to protect against misspecification of working correlation models, which in some cases leads to loss of efficiency or infeasibility of solutions. In this paper, we present a new method named as 'weighted estimating equations (WEE)' for estimating the correlation parameters. The new estimates of correlation parameters are obtained as the solutions of these weighted estimating equations. For some commonly assumed correlation structures, we show that there exists a unique feasible solution to these weighted estimating equations regardless the correlation structure is correctly specified or not. The new feasible estimates of correlation parameters are consistent when the working correlation structure is correctly specified. Simulation results suggest that the new method works well in finite samples.
基金Supported by the National Natural Science Foundation of China (11171263)
文摘Multivariate failure time data are frequently encountered in biomedical research.In this article,we model marginal hazards with accelerated hazards model to analyze multivariate failure time data.Estimating equations are derived analogous to generalized estimating equation method.Under certain regular conditions,the resultant estimators for the regression parameters are shown to be asymptotically normal.Furthermore,we also establish the weak convergence of estimators for the baseline cumulative hazard functions.
文摘Consider a partially linear regression model with an unknown vector parameter , an unknown function g(·), and unknown heteroscedastic error variances. Chen, You<SUP>[23]</SUP> proposed a semiparametric generalized least squares estimator (SGLSE) for , which takes the heteroscedasticity into account to increase efficiency. For inference based on this SGLSE, it is necessary to construct a consistent estimator for its asymptotic covariance matrix. However, when there exists within-group correlation, the traditional delta method and the delete-1 jackknife estimation fail to offer such a consistent estimator. In this paper, by deleting grouped partial residuals a delete-group jackknife method is examined. It is shown that the delete-group jackknife method indeed can provide a consistent estimator for the asymptotic covariance matrix in the presence of within-group correlations. This result is an extension of that in [21].
基金supported by the National Key Research and Development Plan under Grant No.2016YFC0800100the National Science Foundation of China under Grant Nos.11671374,71771203,71631006
文摘Based on the generalized estimating equation approach,the authors propose a parsimonious mean-covariance model for longitudinal data with autoregressive and moving average error process,which not only unites the existing autoregressive Cholesky factor model and moving average Cholesky factor model but also provides a wide variety of structures of covariance matrix.The resulting estimators for the regression coefficients in both the mean and the covariance are shown to be consistent and asymptotically normally distributed under mild conditions.The authors demonstrate the effectiveness,parsimoniousness and desirable performance of the proposed approach by analyzing the CD4-I-cell counts data set and conducting extensive simulations.
基金Supported by the National Natural Science Foundation of China(11071022)Science and Technology Project of Hubei Provincial Department of Education(Q20122202)
文摘In this paper, we introduce a generalized Liu estimator and jackknifed Liu estimator in a linear regression model with correlated or heteroscedastic errors. Therefore, we extend the Liu estimator. Under the mean square error(MSE), the jackknifed estimator is superior to the Liu estimator and the jackknifed ridge estimator. We also give a method to select the biasing parameter for d. Furthermore, a numerical example is given to illustvate these theoretical results.
基金Supported by the National Natural Science Foundation of China (No.40574003) the National Natural Science of Hunan (NO.03JJY3065).
文摘This paper studies estimation and serial correlation test of a semiparametric varying-coefficient partially linear EV model of the form Y = X^Tβ +Z^Tα(T) +ε,ξ = X + η with the identifying condition E[(ε,η^T)^T] =0, Cov[(ε,η^T)^T] = σ^2Ip+1. The estimators of interested regression parameters /3 , and the model error variance σ2, as well as the nonparametric components α(T), are constructed. Under some regular conditions, we show that the estimators of the unknown vector β and the unknown parameter σ2 are strongly consistent and asymptotically normal and that the estimator of α(T) achieves the optimal strong convergence rate of the usual nonparametric regression. Based on these estimators and asymptotic properties, we propose the VN,p test statistic and empirical log-likelihood ratio statistic for testing serial correlation in the model. The proposed statistics are shown to have asymptotic normal or chi-square distributions under the null hypothesis of no serial correlation. Some simulation studies are conducted to illustrate the finite sample performance of the proposed tests.
文摘Let Y have an n-variate normal distribution with covariance matrim σ2I and mean vector Xβ,where X is a known n×p matrix. The problem of estimating θ=σ2+β′X′CXβ is studied. The admissibility and inadmissibility of the estimators of the form bS2+β′X′CXβ, where β= (X′X) ˉX′Y and S2=(Y-Xβ)′(Y-Xβ), are established. Another class of admissible quadratic estimators of θ is derived.