Empirical Likelihood for Censored Linear Regression and Variable Selection
نویسندگان
چکیده
منابع مشابه
An Empirical Likelihood Method for Semiparametric Linear Regression with Right Censored Data
This paper develops a new empirical likelihood method for semiparametric linear regression with a completely unknown error distribution and right censored survival data. The method is based on the Buckley-James (1979) estimating equation. It inherits some appealing properties of the complete data empirical likelihood method. For example, it does not require variance estimation which is problema...
متن کاملInferences in Censored Cost Regression Models with Empirical Likelihood
In many studies of health economics, we are interested in the expected total cost over a certain period for a patient with given characteristics. Problems can arise if cost estimation models do not account for distributional aspects of costs. Two such problems are (1) the skewed nature of the data, and (2) censored observations. In this paper we propose an empirical likelihood (EL) method for c...
متن کاملVariable Selection in Partly Linear Regression Model with Diverging Dimensions for Right Censored Data.
Recent biomedical studies often measure two distinct sets of risk factors: low-dimensional clinical and environmental measurements, and high-dimensional gene expression measurements. For prognosis studies with right censored response variables, we propose a semiparametric regression model whose covariate effects have two parts: a nonparametric part for low-dimensional covariates, and a parametr...
متن کاملEmpirical likelihood inference for censored median regression with weighted empirical hazard functions
In recent years, median regression models have been shown to be useful for analyzing a variety of censored survival data in clinical trials. For inference on the regression parameter, there have been a variety of semiparametric procedures. However, the accuracy of such procedures in terms of coverage probability can be quite low when the censoring rate is heavy. In this paper, based on weighted...
متن کاملVariable Selection for Linear Transformation Models via Penalized Marginal Likelihood
We study the problem of variable selection for linear transformation models, a class of general semiparametric models for censored survival data. The penalized marginal likelihood methods with shrinkage-type penalties are proposed to automate variable selection in linear transformation models; we consider the LASSO penalty and propose a new penalty called the adaptive-LASSO (ALASSO). Unlike the...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Scandinavian Journal of Statistics
سال: 2015
ISSN: 0303-6898
DOI: 10.1111/sjos.12137