A generalized partially linear framework for variance functions
type: Item
instance of: scholarly article
title: A generalized partially linear framework for variance functions
zbMATH Open document ID: 1408.62068
author: Yixin Fang
author: Heng Lian
author: Hua Liang
published in: Annals of the Institute of Statistical Mathematics
publication date: 2018-09-25T00:00:00Z
review text: The authors are concerned with the so-called variance function generalized partially linear model (VFGPLM), a heteroscedastic regression model where both the mean function and the variance function are modeled in a partially linear manner. A two-step estimation procedure for the parameters of the linear part of the mean and the variance function, respectively, is proposed. The authors prove asymptotic normality and analyze (asymptotic) efficiency of the resulting estimators. Finally, the finite-sample behavior of the proposed estimators is illustrated by means of computer simulations and by an empirical example.
reviewed by: Thorsten Dickhaus
Mathematics Subject Classification ID: 62G08
Mathematics Subject Classification ID: 62J12
Mathematics Subject Classification ID: 62G20
zbMATH DE Number: 6943461
zbMATH Keywords: efficiency
zbMATH Keywords: generalized least squares
zbMATH Keywords: kernel regression
zbMATH Keywords: profiling
MaRDI profile type: Publication
full work available at URL: s10463-017-0619-8
OpenAlex ID: W2762972346
cites work: Using residuals robustly I: Tests for heteroscedasticity, nonlinearity
cites work: Q4277836
cites work: Correcting Inhomogeneity of Variance with Power Transformation Weighting
cites work: An Analysis for Unreplicated Fractional Factorials
cites work: Adaptive variance function estimation in heteroscedastic nonparametric regression
cites work: Adapting for heteroscedasticity in linear models
cites work: Variances Are Not Always Nuisance Parameters
cites work: Generalized Partially Linear Single-Index Models
cites work: Semiparametric weighted lease squares
cites work: Robust estimation in heteroscedastic linear models
cites work: Q3995078
cites work: Convergence rates for parametric components in a partly linear model
cites work: Variance Function Estimation
cites work: Estimation for a linear regression model with unknown diagonal covariance matrix
cites work: Q3825946
cites work: Testing Parametric versus Semiparametric Modeling in Generalized Linear Models
cites work: Q4516961
cites work: Nonparametric and semiparametric models.
cites work: Q3761476
cites work: Parametric and semiparametric approaches to testing for seasonal trend in serial count data
cites work: Limiting distributions for \(L_1\) regression estimators under general conditions
cites work: Variance Function Partially Linear Single-Index Models
cites work: Mixtures of g Priors for Bayesian Variable Selection
cites work: Estimation in a semiparametric partially linear errors-in-variables model
cites work: Estimation and testing for partially linear single-index models
cites work: Doubly Robust and Efficient Estimators for Heteroscedastic Partially Linear Single-Index Models Allowing high Dimensional Covariates
cites work: Efficient semiparametric estimator for heteroscedastic partially linear models
cites work: The Asymptotic Variance of Semiparametric Estimators
cites work: Fitting a bivariate additive model by local polynomial regression
cites work: Root-N-Consistent Semiparametric Regression
cites work: Quasi-likelihood Estimation in Semiparametric Models
cites work: Q3823646
cites work: Unified LASSO Estimation by Least Squares Approximation
cites work: Estimation and variable selection for generalized additive partial linear models
cites work: Semi-parametric estimation of partially linear single-index models
cites work: Semiparametric Models for Longitudinal Data with Application to CD4 Cell Numbers in HIV Seroconverters
DOI: 10.1007/S10463-017-0619-8
description: scientific article; zbMATH DE number 6943461