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- Title
Ridge Stochastic Restricted Estimators in Semiparametric Linear Measurement Error Models.
- Authors
Emami, Hadi
- Abstract
In this article we consider the stochastic restricted ridge estimation in semi- parametric 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 is established. Also, necessary and sufficient conditions, for the superiority of the pro- posed 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.
- Subjects
RIDGE regression (Statistics); ERRORS-in-variables models; STOCHASTIC processes; ASYMPTOTIC normality; MONTE Carlo method; MULTICOLLINEARITY
- Publication
Journal of the Iranian Statistical Society, 2018, Vol 17, Issue 2, p181
- ISSN
1726-4057
- Publication type
Article
- DOI
10.29252/jirss.17.2.9