An unbiased estimator with prior information
Adewale F. Lukman, Kayode Ayinde, Benedicta Aladeitan, Bamidele Rasak
Abstract
The ordinary least square (OLS) estimator suffers a breakdown in the presence of multicollinearity. The estimator is still unbiased but possesses a significant variance. In this study, we proposed an unbiased modified ridge-type estimator as an alternative to the OLS estimator and the biased estimators for handling multicollinearity in linear regression models. The properties of this new estimator were derived. The estimator is also unbiased with minimum variance. A real-life application to the higher heating value of poultry waste from proximate analysis and simulation study generally supported the findings.
Topics & Concepts
Minimum-variance unbiased estimatorMulticollinearityBias of an estimatorStein's unbiased risk estimateEfficient estimatorEstimatorStatisticsMathematicsOrdinary least squaresMean squared errorConsistent estimatorTrimmed estimatorVariance inflation factorEconometricsLinear regressionAdvanced Statistical Methods and ModelsSpectroscopy and Chemometric AnalysesAdvanced Statistical Process Monitoring