Volume 17, Issue 2 (12-2018)                   JIRSS 2018, 17(2): 181-203 | Back to browse issues page


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Emami H. Ridge Stochastic Restricted Estimators in Semiparametric Linear Measurement Error Models. JIRSS. 2018; 17 (2) :181-203
URL: http://jirss.irstat.ir/article-1-442-en.html
Department of Statistics, University of Zanjan, Zanjan, Iran
Abstract:   (955 Views)

‎In this article we consider the stochastic restricted ridge estimation in semiparametric linear models when the covariates are measured with additive errors‎. ‎The development of penalized corrected likelihood method in such model is the basis for derivation of ridge estimates‎. ‎The asymptotic normality of the resulting estimates are established‎. ‎Also‎, ‎necessary and sufficient conditions‎, ‎for the superiority of the proposed estimator over its counterpart‎, ‎for selecting the ridge parameter k are obtained‎. ‎A Monte Carlo simulation study is also performed to illustrate the finite sample performance of the proposed procedures‎. ‎Finally theoretical results are applied to Egyptian pottery Industry data set.

Full-Text [PDF 497 kb]   (76 Downloads)    
Type of Study: Original Paper | Subject: 62Jxx: Linear inference, regression
Received: 2017/07/20 | Accepted: 2018/02/5 | Published: 2018/04/21

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