Forward Variable Selection for Sparse Ultra-High Dimensional Varying Coefficient Models

نویسندگان
چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Forward variable selection for sparse ultra-high dimensional varying coefficient models

Varying coefficient models have numerous applications in a wide scope of scientific areas. While enjoying nice interpretability, they also allow flexibility in modeling dynamic impacts of the covariates. But, in the new era of big data, it is challenging to select the relevant variables when there are a large number of candidates. Recently several work are focused on this important problem base...

متن کامل

Variable selection in high-dimensional quantile varying coefficient models

In this paper, we propose a two-stage variable selection procedure for high dimensional quantile varying coefficient models. The proposed method is based on basis function approximation and LASSO-type penalties.We show that the first stage penalized estimator with LASSO penalty reduces the model from ultra-high dimensional to a model that has size close to the true model, but contains the true ...

متن کامل

Variable Selection and Estimation in High-dimensional Varying-coefficient Models.

Nonparametric varying coefficient models are useful for studying the time-dependent effects of variables. Many procedures have been developed for estimation and variable selection in such models. However, existing work has focused on the case when the number of variables is fixed or smaller than the sample size. In this paper, we consider the problem of variable selection and estimation in vary...

متن کامل

Variable selection for varying coefficient models with the sparse regularization

Varying-coefficient models are useful tools for analyzing longitudinal data. They can effectively describe a relationship between predictors and responses repeatedly measured. We consider the problem of selecting variables in the varying-coefficient models via the adaptive elastic net regularization. Coefficients given as functions are expressed by basis expansions, and then parameters involved...

متن کامل

Profile forward regression screening for ultra-high dimensional semiparametric varying coefficient partially linear models

In this paper, we consider semiparametric varying coefficient partially linear models when the predictor variables of the linear part are ultra-high dimensional where the dimensionality grows exponentially with the sample size. We propose a profile forward regression (PFR) method to perform variable screening for ultra-high dimensional linear predictor variables. The proposed PFR algorithm can ...

متن کامل

ذخیره 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: Journal of the American Statistical Association

سال: 2016

ISSN: 0162-1459,1537-274X

DOI: 10.1080/01621459.2015.1080708