Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/32592
Title: Adaptive testing for the partially linear single-index model with error-prone linear covariates
Authors: Huang, Z
Shao, Q
Pang, Z
Lin, B
Keywords: Likelihood ratio method
Measurement error
Penalty function
SCAD
Single-index model
Issue Date: 2015
Publisher: Elsevier
Source: Statistical methodology, 2015, v. 25, p. 51-58 How to cite?
Journal: Statistical Methodology 
Abstract: Adaptive testing for the partially linear single-index model (PLSIM) with error-prone linear covariates is considered.This is a fundamentally important and interesting problem for the current model because existing literature often assumes that the model structure is known before making inferences.In practice,this may result in an incorrect inference on the PLSIM.In this study, we explore whether the link function satisfies some special shape constraints by using an efficient penalized estimating method.For this we propose a model structure selection method by constructing a new testing statistic in the current setting with measurement error,which may enhance the flexibility and predictive power of this model under the case that one can correctly choose an adaptive shape and model structure.The finite sample performance of the proposed methodology is investigated by using some simulation studies and a real example from the Framingham Heart Study.
URI: http://hdl.handle.net/10397/32592
ISSN: 1572-3127
DOI: 10.1016/j.stamet.2014.12.002
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

1
Last Week
0
Last month
0
Citations as of Nov 21, 2018

WEB OF SCIENCETM
Citations

1
Last Week
0
Last month
0
Citations as of Nov 16, 2018

Page view(s)

99
Last Week
1
Last month
Citations as of Nov 19, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.