Semiparametric Regression with Time-dependent Coefficients for Failure Time Data Analysis.
نویسندگان
چکیده
We propose a working independent profile likelihood method for the semiparametric time-varying coefficient model with correlation. Kernel likelihood is used to estimate time-varying coefficient. Profile likelihood for the parametric coefficient is formed by plugging in the nonparametric estimator. For independent data, the estimator is asymptotically normal and achieves the asymptotic semiparametric efficiency bound. We evaluate the performance of proposed nonparametric kernel estimator and the profile estimator, and apply the method to the western Kenya parasitemia data.
منابع مشابه
Robust high-dimensional semiparametric regression using optimized differencing method applied to the vitamin B2 production data
Background and purpose: By evolving science, knowledge, and technology, we deal with high-dimensional data in which the number of predictors may considerably exceed the sample size. The main problems with high-dimensional data are the estimation of the coefficients and interpretation. For high-dimension problems, classical methods are not reliable because of a large number of predictor variable...
متن کاملBootstrap of a Semiparametric Partially Linear Model with Autoregressive Errors
This paper is concerned with a semiparametric partially linear regression model with unknown regression coefficients, an unknown nonparametric function for the non-linear component, and unobservable serially correlated random errors. The random errors are modeled by an autoregressive time series. We show that the distributions of the feasible semiparametric generalized least squares estimator o...
متن کاملA Bayesian Semiparametric Transformation Model Incorporating Frailties
We describe a Bayesian semiparametric (failure time) transformation model for which an unknown monotone transformation of failure times is assumed linearly dependent on observed covariates with an unspecified error distribution. The two unknowns: the monotone transformation and error distribution are assigned prior distributions with large supports. Our class of regression model includes the pr...
متن کاملNonparametric and Semiparametric Analysis of Current Status Data Subject to Outcome Misclassification.
In this article, we present nonparametric and semiparametric methods to analyze current status data subject to outcome misclassification. Our methods use nonparametric maximum likelihood estimation (NPMLE) to estimate the distribution function of the failure time when sensitivity and specificity are known and may vary among subgroups. A nonparametric test is proposed for the two sample hypothes...
متن کاملEstimation in the l1-Regularized Accelerated Failure Time Model
This note variable selection in the semiparametric linear regression model for censored data. Semiparametric linear regression for censored data is a natural extension of the linear model for uncensored data; however, random censoring introduces substantial theoretical and numerical challenges. By now, a number of authors have made significant contributions for estimation and inference in the s...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Statistica Sinica
دوره 20 2 شماره
صفحات -
تاریخ انتشار 2010