Kernel ridge prediction method in partially linear mixed measurement error model
- 13 May 2022
- journal article
- research article
- Published by Taylor & Francis Ltd in Communications in Statistics - Simulation and Computation
Abstract
In this article, a new kernel prediction method by using ridge regression approach is suggested to combat multicollinearity and the impacts of its existence on various views of partially linear mixed measurement error model. We derive the necessary and sufficient condition for the superiority of the linear combinations of the predictors in the sense of the matrix mean square error criterion and give the selection of the ridge biasing parameter. The asymptotic normality condition is investigated and the unknown covariance matrix of measurement errors circumstance is handled. A real data analysis together with a Monte Carlo simulation study is made to assess endorsement of the kernel ridge prediction method.Keywords
This publication has 28 references indexed in Scilit:
- A Small Area Predictor under Area-Level Linear Mixed Models with RestrictionsCommunications in Statistics - Theory and Methods, 2012
- Difference based ridge and Liu type estimators in semiparametric regression modelsJournal of Multivariate Analysis, 2012
- SHRINKAGE, PRETEST AND ABSOLUTE PENALTY ESTIMATORS IN PARTIALLY LINEAR MODELSAustralian & New Zealand Journal of Statistics, 2007
- Average Information REML: An Efficient Algorithm for Variance Parameter Estimation in Linear Mixed ModelsBiometrics, 1995
- That BLUP is a Good Thing: The Estimation of Random EffectsStatistical Science, 1991
- Corrected score function for errors-in-variables models: Methodology and application to generalized linear modelsBiometrika, 1990
- A simulation study of ridge and other regression estimatorsCommunications in Statistics - Theory and Methods, 1976
- A Monte Carlo Evaluation of Some Ridge-Type EstimatorsJournal of the American Statistical Association, 1975
- Ridge Regression: Biased Estimation for Nonorthogonal ProblemsTechnometrics, 1970
- The Estimation of Environmental and Genetic Trends from Records Subject to CullingBiometrics, 1959