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Analysis of Two-sample Censored Data Using a semiparametric Mixture model

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Acta Mathematicae Applicatae Sinica 2009年第3期25卷 389-398页

作者： Gang Li Chien-tai Lin Department of Biostatistics University of California Los Angeles CA 90095 U.S.A. Department of Mathematics Tamkang University Tamsui 251 Taiwan

In this article we study a semiparametric mixture model for the two-sample problem with right censored data. The model implies that the densities for the continuous outcomes are related by a parametric tilt but otherwise unspecified. It provides a useful alternative to the Cox （1972） proportional hazards model for the comparison of treatments based on right censored survival data. We propose an iterative algorithm for the semiparametric maximum likelihood estimates of the parametric and nonparametric components of the model. The performance of the proposed method is studied using simulation. We illustrate our method in an application to melanoma.

关键词： Biased sampling EM algorithm maximum likelihood estimation mixture model semiparametric model

Estimation of semiparametric model with Censored Data

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Estimation of Semiparametric Model with Censored Data

ON BAHADUR ASYMPTOTIC EFFICIENCY IN A semiparametric REGRESSION model

2010 Chinese Control and Decision Conference

作者： Chen Zhongwei and Lu Yiqiang Institute of Electronic Technology,the PLA Information Engineering University,Zhengzhou,450004

For semiparametric regression model with censored data,we firstly propose the estimators of parametric and nonparametric part by the use of kernel method based on distribution function and least squares ***,the asympt... 详细信息

关键词： semiparametric model censored data asymptotic normality strong consistency

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Acta Mathematicae Applicatae Sinica 1995年第2期11卷 172-183页

作者：梁华成平 the Chinese Academy of Science

In this paper. the authors consider Bahadur asymptotic efficiency of LS estimators βof β, which is an unknown parameter vector in the semiparametric regression model Y=HTβ+g(T)+ε,where g is an unknown Holder continuous function, ε is a random error, X is a random vector in Rk, T is a random variable in [0,1], X and T are independent.

关键词： Bahadur efficiency semiparametric model piecewise polynomial

Variable Selection for the Partial Linear Single-Index model

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Acta Mathematicae Applicatae Sinica 2017年第2期33卷 373-388页

作者： Wu WANG Zhong-yi ZHU Department of Statistics School of Management Fudan University Shanghai 200433 China

In this paper, we consider the issue of variable selection in partial linear single-index models under the assumption that the vector of regression coefficients is sparse. We apply penalized spline to estimate the nonparametric function and SCAD penalty to achieve sparse estimates of regression parameters in both the linear and single-index parts of the model. Under some mild conditions, it is shown that the penalized estimators have oracle property, in the sense that it is asymptotically normal with the same mean and covariance that they would have if zero coefficients are known in advance. Our model owns a least square representation, therefore standard least square programming algorithms can be implemented without extra programming efforts. In the meantime, parametric estimation, variable selection and nonparametric estimation can be realized in one step, which incredibly increases computational stability. The finite sample performance of the penalized estimators is evaluated through Monte Carlo studies and illustrated with a real data set.

关键词： nonparametric link function SCAD penalty semiparametric model spline estimation variable selection

A Simulation-Extrapolation Approach to the Analysis of Interval-Censored Failure Time Data with Mis-Measured Covariates

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Journal of Systems Science & Complexity 2024年第6期37卷 2721-2737页

作者： FENG Fan ZHAO Shishun LI Shuwei SUN Jianguo School of Economics and Statistics Guangzhou University School of Mathematics Jilin University School of Mathematics Jilin University Department of Statistics University of Missouri

Interval-censored failure time data arise frequently in periodical follow-up studies including clinical trials and epidemiological surveys. In addition, some covariates may be subject to measurement errors due to the instrumental contamination, biological variation or other reasons. For the analysis of interval-censored data with mis-measured covariates, the existing methods either assume a parametric model or rely on the availability of replicated surrogate measurements for the error-prone covariate, which both have obvious limitations. To overcome these shortcomings, the authors propose a simulation-extrapolation estimation procedure under a general class of transformation models. The resulting estimators are shown to be consistent and asymptotically normal. The numerical results obtained from a simulation study indicate that the proposed method performs reasonably well in *** particular, the proposed method can reduce the estimation bias given by the naive method that does not take measurement errors into account. Finally, the proposed method is applied to a real data set on hypobaric decompression sickness.

关键词： Interval censoring measurement error regression analysis semiparametric model SIMEX

Generalized Penalized Least Squares and Its Statistical Characteristics

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Geo-Spatial Information Science 2006年第4期9卷 255-259页

作者： DING Shijun TAO Benzao School of Geodesy and Geomaties Wuhan University 129 Luoyu Road Wuhan 430079 China 不详

The solution properties of semiparametric model are analyzed, especially that penalized least squares for semiparametric model will be invalid when the matrix B^TPB is ill-posed or singular. According to the principle of ridge estimate for linear parametric model, generalized penalized least squares for semiparametric model are put forward, and some formulae and statistical properties of estimates are derived. Finally according to simulation examples some helpful conclusions are drawn.

关键词： semiparametric model penalized least squares generalized penalized least squares statistical property ill-posed matrix ridge estimate

Personalized treatment selection via the covariate-specific treatment effect curve for longitudinal data

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Statistical Theory and Related Fields 2021年第3期5卷 253-264页

作者： Yanghui Liu Riquan Zhang Shujie Ma Xiuzhen Zhang School of Statistics East China Normal UniversityShanghaiPeople’s Republic of China Department of Statistics University of CaliforniaRiversideCAUSA

Treatment selection based on patient characteristics has been widely recognised in modern *** this paper,we propose a generalised partially linear single-index mixed-effects modelling strategy for treatment selection and heterogeneous treatment effect estimation in longitudinal clinical and observational *** model the treatment effect as an unknown functional curve of a weighted linear combination of time-dependent *** method enables us to investigate covariate-specific treatment effects and make personalised treatment selection in a flexible *** develop a method that combines local linear regression and penalised quasi-likelihood to estimate the weight for each covariate,the unknown treatment effect curve and the parameters for *** on pointwise confidence intervals for the treatment effect curve,we can make individualised treatment decisions from the information of patient characteristics.A simulation study is conducted to evaluate finite sample performance of the proposed *** also illustrate the method via analysis of a real data example.

关键词： personalized medicine treatment selection semiparametric model longitudinal data

Treatment recommendation and parameter estimation under single-index contrast function

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Statistical Theory and Related Fields 2017年第2期1卷 171-181页

作者： Cui Xiong Menggang Yu Jun Shao School of Statistics East China Normal UniversityShanghaiChina Department of Biostatistics and Medical Informatics University of Wisconsin-MadisonMadisonWIUSA Department of Statistics University of Wisconsin-MadisonMadisonWIUSA

In this article, we consider a semiparametric model for contrast function which is defined asthe conditional expected outcome difference under comparative treatments. The contrast function can be used to recommend treatment for better average outcomes. Existing approachesmodel the contrast function either parametrically or nonparametrically. We believe our approachimproves interpretability over the non-parametric approach while enhancing robustness overthe parametric approach. Without explicit estimation of the nonparametric part of our model,we show that a kernel-based method can identify the parametric part up to a multiplying constant. Such identification suffices for treatment recommendation. Our method is also extendedto high-dimensional settings. We study the asymptotics of the resulting estimation procedure inboth low- and high-dimensional cases. We also evaluate our method in simulation studies andreal data analyses.

关键词： Kernel weighting LASSO penalty personalised medicine semiparametric model

On Feedback Capability of Uncertain semiparametric Systems

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On Feedback Capability of Uncertain Semiparametric Systems