Influence Diagnostics in Partially Varying-Coefficient Models

When a real-world data set is fitted to a specific type of models, it is often encountered that one or a set of observations have undue influence on the model fitting, which may lead to misleading conclusions. Therefore, it is necessary for data analysts to identify these influential observations and assess their impact on various aspects of model fitting. In this paper, one type of modified Cook＇s distances is defined to gauge the influence of one or a set observations on the estimate of the constant coefficient part in partially varying- coefficient models, and the Cook＇s distances are expressed as functions of the corresponding residuals and leverages. Meanwhile, a bootstrap procedure is suggested to derive the reference values for the proposed Cook＇s distances. Some simulations are conducted, and a real-world data set is further analyzed to examine the performance of the proposed method. The experimental results are satisfactory.

STRONG CONVERGENCE RATES OF SEVERAL ESTIMATORS IN SEMIPARAMETRIC VARYING-COEFFICIENT PARTIALLY LINEAR MODELS

This article is concerned with the estimating problem of semiparametric varyingcoefficient partially linear regression models. By combining the local polynomial and least squares procedures Fan and Huang （2005） proposed a profile least squares estimator for the parametric component and established its asymptotic normality. We further show that the profile least squares estimator can achieve the law of iterated logarithm. Moreover, we study the estimators of the functions characterizing the non-linear part as well as the error variance. The strong convergence rate and the law of iterated logarithm are derived for them, respectively.

ASYMPTOTIC PROPERTIES OF ESTIMATORS IN PARTIALLY LINEAR SINGLE-INDEX MODEL FOR LONGITUDINAL DATA

In this article, a partially linear single-index model /or longitudinal data is investigated. The generalized penalized spline least squares estimates of the unknown parameters are suggested. All parameters can be estimated simultaneously by the proposed method while the feature of longitudinal data is considered. The existence, strong consistency and asymptotic normality of the estimators are proved under suitable conditions. A simulation study is conducted to investigate the finite sample performance of the proposed method. Our approach can also be used to study the pure single-index model for longitudinal data.

ESTIMATORS AND SOME BEHAVIORS FORA PARTIALLY LINEAR MODEL WITH CENSORED DATA

This paper considers the local linear regression estimators for partially linear model with censored data. Which have some nice large-sample behaviors and are easy to implement. By many simulation runs, the author also found that the estimators show remarkable in the small sample case yet.

Can preoperative planning using IRIS™three-dimensional anatomical virtual models predict operative findings during robot-assisted partial nephrectomy?

Objective To evaluate the predictive validity of IRIS™(Intuitive Surgical®,Sunnyvale,CA,USA)as a planning tool for robot-assisted partial nephrectomy(RAPN)by assessing the degree of overlap with intraoperative execution.Methods Thirty-one patients scheduled for RAPN by four experienced urologists were enrolled in a prospective study.Prior to surgery,urologists reviewed the IRIS™three-dimensional model on an iphone Operating System(iOS)app and completed a questionnaire outlining their surgical plan including surgical approach,and ischemia technique as well as confidence in executing this plan.Postoperatively,questionnaires assessing the procedural approach,clinical utility,efficiency,and effectiveness of IRIS™were completed.The degree of overlap between the preoperative and intraoperative questionnaires and between the planned approach and actual execution of the procedure was analyzed.Questionnaires were answered on a 5-point Likert scale and scores of 4 or greater were considered positive.Results Mean age was 65.1 years with a mean tumor size of 27.7 mm(interquartile range 17.5-44.0 mm).Hilar tumors consisted of 32.3%;48.4%of patients had R.E.N.A.L.nephrometry scores of 7-9.On preoperative questionnaires,the surgeons reported that in 67.7%cases they were confident that they can perform the procedure successfully,and on intraoperative questionnaires,the surgeons reported that in 96.8%cases IRIS™helped achieve good spatial sensation of the anatomy.There was a high degree of overlap between preoperative and intraoperative questionnaires for the surgical approach,interpreting anatomical details and clinical utility.When comparing plans for selective or off-clamp,the preoperative plan was executed in 90.0%of cases intraoperatively.Conclusion A high degree of overlap between the preoperative surgical approach and intraoperative RAPN execution was found using IRIS™.This is the first study to evaluate the predictive accuracy of IRIS™during RAPN by comparing preoperative plan and intraoperative execution.

New Method of Total Ionizing Dose Compact Modeling in Partially Depleted Silicon-on-Insulator MOSFETs

On the basis of a detailed discussion of the development of total ionizing dose （TID） effect model, a new commercial-model-independent TID modeling approach for partially depleted silicon-on-insulator metal-oxide- semiconductor field effect transistors is developed. An exponential approximation is proposed to simplify the trap charge calculation. Irradiation experiments with 60Co gamma rays for IO and core devices are performed to validate the simulation results. An excellent agreement of measurement with the simulation results is observed.

Variable Selection of Partially Linear Single-index Models

In this article, we study the variable selection of partially linear single-index model(PLSIM). Based on the minimized average variance estimation, the variable selection of PLSIM is done by minimizing average variance with adaptive l1 penalty. Implementation algorithm is given. Under some regular conditions, we demonstrate the oracle properties of aLASSO procedure for PLSIM. Simulations are used to investigate the effectiveness of the proposed method for variable selection of PLSIM.

Double-Penalized Quantile Regression in Partially Linear Models

In this paper, we propose the double-penalized quantile regression estimators in partially linear models. An iterative algorithm is proposed for solving the proposed optimization problem. Some numerical examples illustrate that the finite sample performances of proposed method perform better than the least squares based method with regard to the non-causal selection rate (NSR) and the median of model error (MME) when the error distribution is heavy-tail. Finally, we apply the proposed methodology to analyze the ragweed pollen level dataset.

Statistical Diagnostic for Varying-Coefficient Single-Index Models Based on Empirical Likelihood Method

Varying-coefficient single-index model( VCSIM) avoids the so-called "curse of dimensionality " and is flexible enough to include several important statistical models. This paper considers statistical diagnosis for VCSIM. First,the parametric estimation equation is established based on empirical likelihood. Then,some diagnosis statistics are defined. At last, an example is given to illustrate all the results.

Testing Linearity of Nonparametric Component in Partially Linear Model

In this paper, we propose the test statistic to check whether the nonparametric function in partially linear models is linear or not. We estimate the nonparametric function in alternative by using the local linear method, and then estimate the parameters by the two stage method. The test statistic under the null hypothesis is calculated, and it is shown to be asymptotically normal.

Bayesian partial pooling to reduce uncertainty in overcoring rock stress estimation

The state of in situ stress is a crucial parameter in subsurface engineering,especially for critical projects like nuclear waste repository.As one of the two ISRM suggested methods,the overcoring(OC)method is widely used to estimate the full stress tensors in rocks by independent regression analysis of the data from each OC test.However,such customary independent analysis of individual OC tests,known as no pooling,is liable to yield unreliable test-specific stress estimates due to various uncertainty sources involved in the OC method.To address this problem,a practical and no-cost solution is considered by incorporating into OC data analysis additional information implied within adjacent OC tests,which are usually available in OC measurement campaigns.Hence,this paper presents a Bayesian partial pooling(hierarchical)model for combined analysis of adjacent OC tests.We performed five case studies using OC test data made at a nuclear waste repository research site of Sweden.The results demonstrate that partial pooling of adjacent OC tests indeed allows borrowing of information across adjacent tests,and yields improved stress tensor estimates with reduced uncertainties simultaneously for all individual tests than they are independently analysed as no pooling,particularly for those unreliable no pooling stress estimates.A further model comparison shows that the partial pooling model also gives better predictive performance,and thus confirms that the information borrowed across adjacent OC tests is relevant and effective.

Preliminary exploration of animal models of congenital choledochal cysts

BACKGROUND Various animal models have been used to explore the pathogenesis of choledochal cysts(CCs),but with little convincing results.Current surgical techniques can achieve satisfactory outcomes for treatment of CCs.Consequently,recent studies have focused more on clinical issues rather than basic research.Therefore,we need appropriate animal models to further basic research.AIM To establish an appropriate animal model that may contribute to the investigation of the pathogenesis of CCs.METHODS Eighty-four specific pathogen-free female Sprague-Dawley rats were randomly allocated to a surgical group,sham surgical group,or control group.A rat model of CC was established by partial ligation of the bile duct.The reliability of the model was confirmed by measurements of serum biochemical indices,morpho-logy of common bile ducts of the rats as well as molecular biology experiments in rat and human tissues.RESULTS Dilation classified as mild(diameter,≥1 mm to<3 mm),moderate(≥3 mm to<10 mm),and severe(≥10 mm)was observed in 17,17,and 2 rats in the surgical group,respectively,while no dilation was observed in the control and sham surgical groups.Serum levels of alanine aminotransferase,aspartate aminotrans-ferase,total bilirubin,direct bilirubin,and total bile acids were significantly elevated in the surgical group as compared to the control group 7 d after surgery,while direct bilirubin,total bilirubin,and gamma-glutamyltransferase were further increased 14 d after surgery.Most of the biochemical indices gradually decreased to normal ranges 28 d after surgery.The protein expression trend of signal transducer and activator of transcription 3 in rat model was consistent with the human CC tissues.CONCLUSION The model of partial ligation of the bile duct of juvenile rats could morphologically simulate the cystic or fusiform CC,which may contribute to investigating the pathogenesis of CC.

Some Asymptotic Properties for Multivariate Partially Linear Models

纸在独立错误下面认为 multivariate 是部分线性的模型，并且为参量的部件和 nonparametric 部件 F 调查 asymptotic 偏爱和变化协变性(洠 ?  ??

Function-on-Partially Linear Functional Additive Models

We consider a functional partially linear additive model that predicts a functional response by a scalar predictor and functional predictors. The B-spline and eigenbasis least squares estimator for both the parametric and the nonparametric components proposed. In the final of this paper, as a result, we got the variance decomposition of the model and establish the asymptotic convergence rate for estimator.

Empirical Likelihood Inference for Generalized Partially Linear Models with Longitudinal Data

In this article, we propose a generalized empirical likelihood inference for the parametric component in semiparametric generalized partially linear models with longitudinal data. Based on the extended score vector, a generalized empirical likelihood ratios function is defined, which integrates the within-cluster?correlation meanwhile avoids direct estimating the nuisance parameters in the correlation matrix. We show that the proposed statistics are asymptotically?Chi-squared under some suitable conditions, and hence it can be used to construct the confidence region of parameters. In addition, the maximum empirical likelihood estimates of parameters and the corresponding asymptotic normality are obtained. Simulation studies demonstrate the performance of the proposed method.

Long-distance propagation of pseudo-partially coherent Gaussian Schell-model beams in atmospheric turbulence

Propagation properties of spatially pseudo-partially coherent Gaussian Schell-model beams through the atmo- spheric turbulence over a long-distance uplink path are studied by numerical simulation. A linear coordination trans- formation is introduced to overcome the window effect and the loss-of-resolution problem. The beam spreading, beam wandering, and intensity scintillation as functions of turbulence strength, source correlation length, and change fre- quency of random phase that models the partial coherence of the source are analyzed. It is found that the beam spreading and the intensity scintillation of the partially coherent beam are less affected by the turbulence than those of the coherent one, but it suffers from a more severe diffractive effect, and the change frequency of random phase has no evident influence on it. The beam wandering is insensitive to the variation of source correlation length, and decreases firstly then goes to a fixed value as the change frequency increases.

Testing Equality of Nonparametric Functions in Two Partially Linear Models

We propose the test statistic to check whether the nonpararnetric functions in two partially linear models are equality or not in this paper. We estimate the nonparametric function both in null hypothesis and the alternative by the local linear method, where we ignore the parametric components, and then estimate the parameters by the two stage method. The test statistic is derived, and it is shown to be asymptotically normal under the null hypothesis.

The Application and Property of Elastic Net Procedure for Partially Linear Models

Variable selection plays an important role in high-dimensional data analysis.But the high-dimensional data often induces the strongly correlated variables problem,which should be properly handled.In this paper,we propose Elastic Net procedure for partially linear models and prove the group effect of its estimate.A simulation study shows that the Elastic Net procedure deals with the strongly correlated variables problem better than the Lasso,ALasso and the Ridge do.Based on the real world data study,we can get that the Elastic Net procedure is particularly useful when the number of predictors pffis much bigger than the sample size n.

Efficient Shrinkage Estimation about the Partially Linear Varying Coefficient Model with Random Effect for Longitudinal Data

In this paper, an efficient shrinkage estimation procedure for the partially linear varying coefficient model (PLVC) with random effect is considered. By selecting the significant variable and estimating the nonzero coefficient, the model structure specification is accomplished by introducing a novel penalized estimating equation. Under some mild conditions, the asymptotic properties for the proposed model selection and estimation results, such as the sparsity and oracle property, are established. Some numerical simulation studies and a real data analysis are presented to examine the finite sample performance of the procedure.

Heterogeneous performance analysis of the new model of CFAR detectors for partially-correlated χ~2-targets

To mitigate the deleterious effects of clutter and jammer, modern radars have adopted adaptive processing techniques such as constant false alarm rate(CFAR) detectors which are widely used to prevent clutter and noise interference from saturating the radar’s display and preventing targets from being obscured.This paper concerns with the detection analysis of the novel version of CFAR schemes(cell-averaging generalized trimmed-mean,CATM) in the presence of additional outlying targets other than the target under research. The spurious targets as well as the tested one are assumed to be fluctuating in accordance with the χ~2-model with two-degrees of freedom. In this situation, the processor performance is enclosed by the swerling models(SWI and SWII). Between these bounds, there is an important class of target fluctuation which is known as moderately fluctuating targets. The detection of this class has many practical applications. Structure of the CATM detector is described briefly. Detection performances for optimal, CAM, CA, trimmed-mean(TM) and ordered-statistic(OS) CFAR strategies have been analyzed and compared for desired probability of false alarm and determined size of the reference window. False alarm rate performance of these processors has been evaluated for different strengths of interfering signal and the effect of correlation among the target returns on the detection and false alarm performances has also been studied. Our numerical results show that, with a proper choice of trimming parameters,the novel model CAM presents an ideal detection performance outweighing that of the Neyman-Pearson detector on condition that the tested target obeys the SWII model in its fluctuation. Although the new models CAS and CAM can be treated as special cases of the CATM algorithm, their multi-target performance is modest even it has an enhancement relative to that of the classical CAcheme. Additionally, they fail to maintain the false alarm rate constant when the operating environment is of type target multiplicity. Moreover, the non-coherent integration of M pulses ameliorates the processor performance either it operates in homogeneous or multi-target environment.