Censored linear model in high dimensions
نویسندگان
چکیده
منابع مشابه
h . ST ] 3 M ay 2 01 4 Censored linear model in high dimensions
Censored data are quite common in statistics and have been studied in depth in the last years (for some early references, see Powell (1984), Muphy et al. (1999), Chay and Powell (2001)). In this paper we consider censored high-dimensional data. High-dimensional models are in some way more complex than their lowdimensional versions, therefore some different techniques are required. For the linea...
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 کاملStrategyproof Linear Regression in High Dimensions
is paper is part of an emerging line of work at the intersection of machine learning and mechanism design, which aims to avoid noise in training data by correctly aligning the incentives of data sources. Specically, we focus on the ubiquitous problem of linear regression, where strategyproof mechanisms have previously been identied in two dimensions. Our main contribution is the discovery of...
متن کاملRegularized Weighted Linear Regression for High-dimensional Censored Data
Survival analysis aims at modeling time to event data which occurs ubiquitously in many biomedical and healthcare applications. One of the critical challenges with modeling such survival data is the presence of censored outcomes which cannot be handled by standard regression models. In this paper, we propose a regularized linear regression model with weighted least-squares to handle the surviva...
متن کاملA linear mixed-effects model for multivariate censored data.
We apply a linear mixed-effects model to multivariate failure time data. Computation of the regression parameters involves the Buckley-James method in an iterated Monte Carlo expectation-maximization algorithm, wherein the Monte Carlo E-step is implemented using the Metropolis-Hastings algorithm. From simulation studies, this approach compares favorably with the marginal independence approach, ...
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: TEST
سال: 2015
ISSN: 1133-0686,1863-8260
DOI: 10.1007/s11749-015-0441-7