Efficient estimation of semiparametric varying-coefficient partially linear transformation model with current status data

We consider a varying-coefficient partially linear transformation model with current status data, which extends several semiparametric models for current status data in the literature. Sieve maximum likelihood estimation method is used to obtain an integrated estimate for both the parametric components and nonparametric components in the model, i.e. the linear regression coefficients, the varying-coefficient functions and the baseline survival function. Under some regularity conditions, the proposed parameter estimators are proved to be semiparametrically efficient and asymptotically normal, and the estimators for the nonparametric functions achieve the optimal rate of convergence. Simulation studies assure the theoretical results, and a real data is reanalysed using the proposed method and it yields new findings.</p

sj-pdf-1-smm-10.1177_09622802231181231 - Supplemental material for A Bayesian genomic selection approach incorporating prior feature ordering and population structures with application to coronary artery disease

Supplemental material, sj-pdf-1-smm-10.1177_09622802231181231 for A Bayesian genomic selection approach incorporating prior feature ordering and population structures with application to coronary artery disease by Xiaotian Dai, Xuewen Lu and Thierry Chekouo in Statistical Methods in Medical Research</p

Variable Selection in a Log–Linear Birnbaum–Saunders Regression Model for High-Dimensional Survival Data via the Elastic-Net and Stochastic EM

The Birnbaum–Saunders (BS) distribution is broadly used to model failure times in reliability and survival analysis. In this article, we propose a simultaneous parameter estimation and variable selection procedure in a log–linear BS regression model for high-dimensional survival data. To deal with censored survival data, we iteratively run a combination of the stochastic EM algorithm (SEM) and variable selection procedure to generate pseudo-complete data and select variables until convergence. Treating pseudo-complete data as uncensored data via SEM makes it possible to incorporate iterative penalized least squares and simplify computation. We demonstrate the efficacy of our method using simulated and real datasets.</p

Automatic variable selection for semiparametric spatial autoregressive model

This article studies the generalized method of moment estimation of semiparametric varying coefficient partially linear spatial autoregressive model. The technique of profile least squares is employed and all estimators have explicit formulas which are computationally convenient. We derive the limiting distributions of the proposed estimators for both parametric and non parametric components. Variable selection procedures based on smooth-threshold estimating equations are proposed to automatically eliminate irrelevant parameters and zero varying coefficient functions. Compared to the alternative approaches based on shrinkage penalty, the new method is easily implemented. Oracle properties of the resulting estimators are established. Large amounts of Monte Carlo simulations confirm our theories and demonstrate that the estimators perform reasonably well in finite samples. We also apply the novel methods to an empirical data analysis.</p

Efficient estimation of a varying-coefficient partially linear proportional hazards model with current status data

We consider a varying-coefficient partially linear proportional hazards model with current status data. The proposed model enables one to examine the extent to which some covariates interact nonlinearly with an exposure variable, while other covariates present linear effects. B-splines are applied to model both the unknown cumulative baseline hazard function and the varying-coefficient functions with and without monotone constraints, depending on the nature of the nonparametric functions. The sieve maximum likelihood estimation method is used to get an integrated estimate for the linear coefficients, the varying-coefficient functions and the cumulative baseline hazard function. The proposed parameter estimators are proved to be semiparametrically efficient and asymptotically normal, and the estimators for the nonparametric functions achieve the optimal rate of convergence. Simulation studies and a real data analysis are used for assessment and illustration.</p