提出了在等电位工作状态下校准多元感应分流器(multiple inductive current divider,MICD)的1种方法。在校准装置中加入由跟随器和电阻组成的I/V变换器,以使MICD各支路间的电流比例与其工作状态保持一致;用参考电流互感器(CT)提供独立...提出了在等电位工作状态下校准多元感应分流器(multiple inductive current divider,MICD)的1种方法。在校准装置中加入由跟随器和电阻组成的I/V变换器,以使MICD各支路间的电流比例与其工作状态保持一致;用参考电流互感器(CT)提供独立的参考电流,以避免影响支路电流的比例关系。在20、55、400 Hz及1 kHz下对11支路MICD校准所得10:1电流比例的比值差合成标准不确定度优于0.2μA/A,相位差合成标准不确定度优于1.5μrad。展开更多
Exclusive hypothesis testing is a new and special class of hypothesis testing.This kind of testing can be applied in survival analysis to understand the association between genomics information and clinical informatio...Exclusive hypothesis testing is a new and special class of hypothesis testing.This kind of testing can be applied in survival analysis to understand the association between genomics information and clinical information about the survival time.Besides,it is well known that Cox's proportional hazards model is the most commonly used model for regression analysis of failure time.In this paper,the authors consider doing the exclusive hypothesis testing for Cox's proportional hazards model with right-censored data.The authors propose the comprehensive test statistics to make decision,and show that the corresponding decision rule can control the asymptotic TypeⅠerrors and have good powers in theory.The numerical studies indicate that the proposed approach works well for practical situations and it is applied to a set of real data arising from Rotterdam Breast Cancer Data study that motivated this study.展开更多
This work studies a proportional hazards model for survival data with ＂long-term survivors＂, in which covariates are subject to linear measurement error. It is well known that the naive estimators from both partial ...This work studies a proportional hazards model for survival data with ＂long-term survivors＂, in which covariates are subject to linear measurement error. It is well known that the naive estimators from both partial and full likelihood methods are inconsistent under this measurement error model. For measurement error models, methods of unbiased estimating function and corrected likelihood have been proposed in the literature. In this paper, we apply the corrected partial and full likelihood approaches to estimate the model and obtain statistical inference from survival data with long-term survivors. The asymptotic properties of the estimators are established. Simulation results illustrate that the proposed approaches provide useful tools for the models considered.展开更多
This paper deals with the problem of accelerometer error estimation and compensation for a three-axis gyro-stabilized camera mount. In a dynamic environment, the aircraft motion acceleration affects the accelerome ：e...This paper deals with the problem of accelerometer error estimation and compensation for a three-axis gyro-stabilized camera mount. In a dynamic environment, the aircraft motion acceleration affects the accelerome ：er output and causes a degradation of attitude steady accuracy. In order to improve control accuracy, this paper proposes a proportional multiple-integral observer- based control strategy to estimate and compensate the accelerometer error. The basic idea of this paper is to approximate the error property by using a q-order polynomial function and extend the error and its derivatives as augmented states. Then a proportional multiple-integral observer is developed to estimate the error, with which the relationship between the error and the imbalance torque is formulated. The estimated value is compared to an angle threshold, the result of which is used to compen- sate the accelerometer output. Through static and vehicle-mounted experiments, it is demonstrated that compared with the tra- ditional method, the proposed method can improve the attitude steady accuracy effectively.展开更多
Multiple testing has gained much attention in high-dimensional statistical theory and applications,and the problem of variable selection can be regarded as a generalization of the multiple testing.It is aiming to sele...Multiple testing has gained much attention in high-dimensional statistical theory and applications,and the problem of variable selection can be regarded as a generalization of the multiple testing.It is aiming to select the important variables among many variables.Performing variable selection in high-dimensional linear models with measurement errors is challenging.Both the influence of high-dimensional parameters and measurement errors need to be considered to avoid severely biases.We consider the problem of variable selection in error-in-variables and introduce the DCoCoLasso-FDP procedure,a new variable selection method.By constructing the consistent estimator of false discovery proportion(FDP)and false discovery rate(FDR),our method can prioritize the important variables and control FDP and FDR at a specifical level in error-in-variables models.An extensive simulation study is conducted to compare DCoCoLasso-FDP procedure with existing methods in various settings,and numerical results are provided to present the efficiency of our method.展开更多
基金supported by the National Natural Science Foundation of China under Grant Nos.11971064,12371262,and 12171374。
文摘Exclusive hypothesis testing is a new and special class of hypothesis testing.This kind of testing can be applied in survival analysis to understand the association between genomics information and clinical information about the survival time.Besides,it is well known that Cox's proportional hazards model is the most commonly used model for regression analysis of failure time.In this paper,the authors consider doing the exclusive hypothesis testing for Cox's proportional hazards model with right-censored data.The authors propose the comprehensive test statistics to make decision,and show that the corresponding decision rule can control the asymptotic TypeⅠerrors and have good powers in theory.The numerical studies indicate that the proposed approach works well for practical situations and it is applied to a set of real data arising from Rotterdam Breast Cancer Data study that motivated this study.
基金supported by the National Nature Science Foundation of China under Grant No.10871084Macquarie University Safety Net grant
文摘This work studies a proportional hazards model for survival data with ＂long-term survivors＂, in which covariates are subject to linear measurement error. It is well known that the naive estimators from both partial and full likelihood methods are inconsistent under this measurement error model. For measurement error models, methods of unbiased estimating function and corrected likelihood have been proposed in the literature. In this paper, we apply the corrected partial and full likelihood approaches to estimate the model and obtain statistical inference from survival data with long-term survivors. The asymptotic properties of the estimators are established. Simulation results illustrate that the proposed approaches provide useful tools for the models considered.
基金supported by the National Natural Science Foundation of China(Grant Nos.61174121,61333005 and 61121003)the Ph.D Programs Foundations of the Ministry of Education China
文摘This paper deals with the problem of accelerometer error estimation and compensation for a three-axis gyro-stabilized camera mount. In a dynamic environment, the aircraft motion acceleration affects the accelerome ：er output and causes a degradation of attitude steady accuracy. In order to improve control accuracy, this paper proposes a proportional multiple-integral observer- based control strategy to estimate and compensate the accelerometer error. The basic idea of this paper is to approximate the error property by using a q-order polynomial function and extend the error and its derivatives as augmented states. Then a proportional multiple-integral observer is developed to estimate the error, with which the relationship between the error and the imbalance torque is formulated. The estimated value is compared to an angle threshold, the result of which is used to compen- sate the accelerometer output. Through static and vehicle-mounted experiments, it is demonstrated that compared with the tra- ditional method, the proposed method can improve the attitude steady accuracy effectively.
文摘Multiple testing has gained much attention in high-dimensional statistical theory and applications,and the problem of variable selection can be regarded as a generalization of the multiple testing.It is aiming to select the important variables among many variables.Performing variable selection in high-dimensional linear models with measurement errors is challenging.Both the influence of high-dimensional parameters and measurement errors need to be considered to avoid severely biases.We consider the problem of variable selection in error-in-variables and introduce the DCoCoLasso-FDP procedure,a new variable selection method.By constructing the consistent estimator of false discovery proportion(FDP)and false discovery rate(FDR),our method can prioritize the important variables and control FDP and FDR at a specifical level in error-in-variables models.An extensive simulation study is conducted to compare DCoCoLasso-FDP procedure with existing methods in various settings,and numerical results are provided to present the efficiency of our method.
