Profile forward regression screening for ultra-high dimensional semiparametric varying coefficient partially linear models
نویسندگان
چکیده
منابع مشابه
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 کاملQuantile Regression in Partially Linear Varying Coefficient Models by Huixia
Semiparametric models are often considered for analyzing longitudinal data for a good balance between flexibility and parsimony. In this paper, we study a class of marginal partially linear quantile models with possibly varying coefficients. The functional coefficients are estimated by basis function approximations. The estimation procedure is easy to implement, and it requires no specification...
متن کاملStatistical Inference for Semiparametric Varying-coefficient Partially Linear Models with Error-prone Linear Covariates
We study semiparametric varying-coefficient partially linear models when some linear covariates are not observed, but ancillary variables are available. Semiparametric profile least-square based estimation procedures are developed for parametric and nonparametric components after we calibrate the error-prone covariates. Asymptotic properties of the proposed estimators are established. We also p...
متن کاملForward 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 کاملNew Efficient Estimation and Variable Selection Methods for Semiparametric Varying-coefficient Partially Linear Models By
The complexity of semiparametric models poses new challenges to statistical inference and model selection that frequently arise from real applications. In this work, we propose new estimation and variable selection procedures for the semiparametric varying-coefficient partially linear model. We first study quantile regression estimates for the nonparametric varyingcoefficient functions and 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: Journal of Multivariate Analysis
سال: 2017
ISSN: 0047-259X
DOI: 10.1016/j.jmva.2016.12.006