View : 35 Download: 0

Semiparametric estimation for partially linear models with ψ-weak dependent errors

Title
Semiparametric estimation for partially linear models with ψ-weak dependent errors
Authors
Hwang E.Shin D.W.
Ewha Authors
신동완황은주
SCOPUS Author ID
신동완scopus
Issue Date
2011
Journal Title
Journal of the Korean Statistical Society
ISSN
1226-3192JCR Link
Citation
vol. 40, no. 4, pp. 411 - 424
Indexed
SCIE; SCOPUS; KCI WOS scopus
Abstract
Semiparametric estimators are developed for a partially linear regression model with ψ-weakly dependent errors. The ψ-weak dependence condition, introduced by Doukhan and Louhich [Doukhan, P., and Louhich, S. (1999). A new weak dependence condition and applications to moment inequalities. Stochastic Processes and their Applications, 84, 313-342], unifies weak dependence conditions such as mixing, association, Gaussian sequences and Bernoulli shifts. The class of ψ-weak dependent processes includes many important nonlinear processes such as stationary threshold autoregressive processes and bilinear processes as well as stationary ARMA processes. Asymptotic normalities are established for semiparametric generalized least squares estimators of the parametric component and for estimators of the nonparametric function. Expansions are obtained for the biases and variances of the estimators. Real data set and simulated data set analyses are provided for a model with a threshold autoregressive error process. © 2011 The Korean Statistical Society.
DOI
10.1016/j.jkss.2011.01.002
Appears in Collections:
자연과학대학 > 통계학전공 > Journal papers
Files in This Item:
There are no files associated with this item.
Export
RIS (EndNote)
XLS (Excel)
XML


qrcode

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

BROWSE