Characterization of admissible linear estimators under extended balanced loss function

summary:In this paper, we study the admissibility of linear estimator of regression coefficient in linear model under the extended balanced loss function (EBLF). The sufficient and necessary condition for linear estimators to be admissible are obtained respectively in homogeneous and non-homogeneous classes. Furthermore, we show that admissible linear estimator under the EBLF is a convex combination of the admissible linear estimator under the sum of square residuals and quadratic loss function

Matris sıralamaları ve istatistikteki uygulamaları

TEZ984Tez (Yüksek Lisans) -- Çukurova Üniversitesi, Adana, 1991.Kaynakça (s. viii,x) var.xii, 65 s. ; 30 cm.

Yanlı regresyon tahmin edicileri ve hata kareleri ortalaması kriterlerine göre karşılaştırmalar

TEZ1821Tez (Doktora) -- Çukurova Üniversitesi, Adana, 1995.Kaynakça (s. xvii-xx) var.xxii, 94 s. ; 30 cm.

Mean square error comparisons of the alternative estimators for the distributed lag models

The finite distributed lag models include highly correlated variablesas well as lagged and unlagged values of the same variables. Someproblems are faced for this model when applying the ordinary leastsquares (OLS) method or econometric models such as Almon and Koyckmodels. The primary aim of this study is to compare the performancesof alternative estimators to the OLS estimator defined by combiningthe Almon estimator with some other estimators according to the meansquare error (MSE) criterion. We use Almon [2] data to illustrate ourtheoretical results.The finite distributed lag models include highly correlated variablesas well as lagged and unlagged values of the same variables. Someproblems are faced for this model when applying the ordinary leastsquares (OLS) method or econometric models such as Almon and Koyckmodels. The primary aim of this study is to compare the performancesof alternative estimators to the OLS estimator defined by combiningthe Almon estimator with some other estimators according to the meansquare error (MSE) criterion. We use Almon [2] data to illustrate ourtheoretical results

THE PREDICTION OF THE TWO PARAMETER RIDGE ESTIMATOR

The prediction of regression model can be adversely affected by multicollinearity. Although biased estimation procedures have been proposed as an alternative to least squares, there has been little analysis of the predictive performance of the resulting equations. Therefore, we discuss the predictive performance of the Two Parameter Ridge (2PR) estimator compared to ordinary least squares, principal components and ridge regression estimators. Also, the theoretical results are illustrated by numerical example and region is established where the 2PR estimator is uniformly superior to the other estimators.The prediction of regression model can be adversely affected by multicollinearity. Although biased estimation procedures have been proposed as an alternative to least squares, there has been little analysis of the predictive performance of the resulting equations. Therefore, we discuss the predictive performance of the Two Parameter Ridge (2PR) estimator compared to ordinary least squares, principal components and ridge regression estimators. Also, the theoretical results are illustrated by numerical example and region is established where the 2PR estimator is uniformly superior to the other estimators

Poisson and negative binomial regression models for zero-inflated data: an experimental study

WOS:000822397600012Count data regression has been widely used in various disciplines, particularly health area. Classical models like Poisson and negative binomial regression may not provide reasonable performance in the presence of excessive zeros and overdispersion problems. Zero-inflated and Hurdle variants of these models can be a remedy for dealing with these problems. As well as zero-inflated and Hurdle models, alternatives based on some biased estimators like ridge and Liu may improve the performance against to multicollinearity problem except excessive zeros and overdispersion. In this study, ten different regression models including classical Poisson and negative binomial regression with their variants based on zero-inflated, Hurdle, ridge and Liu approaches have been compared by using a health data. Some criteria including Akaike information criterion, log-likelihood value, mean squared error and mean absolute error have been used to investigate the performance of models. The results show that the zero-inflated negative binomial regression model provides the best fit for the data. The final model estimations have been obtained via this model and interpreted in detail. Finally, the experimental results suggested that models except the classical models should be considered as powerful alternatives for modelling count and give better insights to the researchers in applying statistics on working similar data structures

Robust Liu-type estimator for regression based on M-estimator

Hasan Ertas was supported by Cukurova University Academic Research Projects (FEF2013D15).The problem of multicollinearity and outliers in the dataset can strongly distort ordinary least-square estimates and lead to unreliable results. We propose a new Robust Liu-type M-estimator to cope with this combined problem of multicollinearity and outliers in the y-direction. Our new estimator has advantages over two-parameter Liu-type estimator, Ridge-type M-estimator, and M-estimator. Furthermore, we give a numerical example and a simulation study to illustrate some of the theoretical results

Two-stage Liu estimator in a simultaneous equations model

Two-stage least squares estimation in a simultaneous equations model has several desirable properties under the problem of multicollinearity. So, various kinds of improved estimation techniques can be developed to deal with the problem of multicollinearity. One of them is ridge regression estimation that can be applied at both stages and defined in Vinod and Ullah [Recent advances in regression methods. New York: Marcel Dekker; 1981]. We propose three different kinds of Liu estimators that are named by their implementation stages. Mean square errors are derived to compare the performances of the mentioned estimators and two different choices of the biasing parameter are offered. Moreover, a numerical example is given with a data analysis based on the Klein Model I and a Monte Carlo experiment is conducted. © 2018 Informa UK Limited, trading as Taylor & Francis Group