Second order asymptotic efficiency in a partial linear model
type: Item
instance of: scholarly article
title: Second order asymptotic efficiency in a partial linear model
zbMATH Open document ID: 0786.62039
DOI: 10.1016/0167-7152(93)90101-N
author: Hua Liang
author: Ping Cheng
published in: Statistics \& Probability Letters
publication date: 1993-12-12T00:00:00Z
review text: This paper considers a model of the form \(Y=X\beta+g(T)+\varepsilon\), where \(X\in R^ 1\) is an explanatory variable that enters linearly, \(\beta\) is an unknown parameter, \(T\) is another explanatory variable that enters in a nonlinear fashion, \(g(\cdot)\) is an unknown smooth function of \(T\) in \(R^ 1\), \((X,T)\) and \(\varepsilon\) are independent and \(\varepsilon\) is the random error with mean 0 and variance \(\sigma^ 2\). The second order asymptotic efficiency of estimators of \(\beta\) based on the maximum likelihood estimator has been studied.
Mathematics Subject Classification ID: 62F12
Mathematics Subject Classification ID: 62J02
Mathematics Subject Classification ID: 62J99
zbMATH DE Number: 445291
zbMATH Keywords: asymptotic median unbiasedness
zbMATH Keywords: semiparametric model
zbMATH Keywords: explanatory variable
zbMATH Keywords: second order asymptotic efficiency
zbMATH Keywords: maximum likelihood estimator
reviewed by: Rahul Mukerjee
MaRDI profile type: Publication
cites work: On the application of the Minkowski‐Farkas theorem to sampling designs
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full work available at URL: 0167-7152(93)90101-n
OpenAlex ID: W2011888642
description: scientific article; zbMATH DE number 445291