Issam Dawoud

Evaluation of the predictive performance of the r-k and r-d class estimators

ABSTRACT Multiple linear regression models are frequently used in predicting unknown values of the response variable y. In this case, a regression models ability to produce an adequate prediction equation is of prime importance. This paper discusses the predictive performance of the r-k and r-d class estimators compared to ordinary least squares (OLS), principal components, ridge regression and Liu estimators and between each other. The theoretical results are illustrated using Portland cement data and a region is established where the r-k and the r-d class estimators are uniformly superior to the other mentioned estimators.

Two Stages Liu Regression Estimator

This paper introduces a new estimator for multicollinearity and autocorrelated errors. We propose the Two Stages Liu estimator (TL) for the multiple linear regression model which suffers from autocorrelation AR(1) and multicollinearity problems. We use a mixed method to apply the two stages least squares procedure (TS) for deriving the TL estimator. We also derive some statistical properties of the TL estimator and comparisons have been conducted using matrix mean square error (MMSE). Furthermore, a Monte Carlo study and a real data are carried out to investigate the performance of the proposed estimator over the others.

The feasible generalized restricted ridge regression estimator

ABSTRACT The presence of autocorrelation in errors and multicollinearity among the regressors have undesirable effects on the least-squares regression. There are a wide range of methods which are proposed to overcome the usefulness of the ordinary least-squares estimator or the generalized least-squares estimator, such as the Stein-rule, restricted least-squares or ridge estimator. Therefore, we introduce a new feasible generalized restricted ridge regression (FGRR) estimator to examine multicollinearity and autocorrelation problems simultaneously for the general linear regression model. We also derive some statistical properties of the FGRR estimator and comparisons have been conducted using matrix mean-square error. Moreover, a Monte Carlo simulation experiment is performed to investigate the performance of the proposed estimator over the others.

Feasible Generalized Stein-Rule Restricted Ridge Regression Estimators

Abstract Several versions of the Stein-rule estimators of the coefficient vector in a linear regression model are proposed in the literature. In the present paper, we propose new feasible generalized Stein-rule restricted ridge regression estimators to examine multicollinearity and autocorrelation problems simultaneously for the general linear regression model, when certain additional exact restrictions are placed on these coefficients. Moreover, a Monte Carlo simulation experiment is performed to investigate the performance of the proposed estimator over the others.

Comment on A Generalized Stochastic Restricted Ridge Regression Estimator

ABSTRACT In this note, we make some comments about the paper of Alheety and Kibria (2014) and correct the wrongly proved Theorems in that paper.

An optimal k of kth MA-ARIMA models under a class of ARIMA model

ABSTRACT In this article, we discuss finding the optimal k of (i) kth simple moving average, (ii) kth weighted moving average, and (iii) kth exponential weighted moving average based on simulated ARIMA(p, d, q) model. We run a simulation using the three above examining methods under specific conditions. The main finding is that 5th exponential weighted moving average (5th EWMA) ARIMA model is the best forecasting model among others, which means the optimal k = 5. For Turkish Telecommunications (TTKOM) stock market, real data reveal the similar results of simulation study.

Evaluation of the predictive performance of the Liu type estimator

ABSTRACT This article discusses the predictive performance of the Liu type (LT) estimator compared to ordinary least squares, principal components, ridge regression, and Liu estimators. The theoretical results are illustrated by a numerical example and a region is established where the LT estimator is uniformly superior to the other mentioned estimators.

An optimal k of kth MA-ARIMA models under AR(p) models

ABSTRACT In this article, we discuss finding the optimal k of (i) kth simple moving average, (ii) kth weighted moving average, and (iii) kth exponential weighted moving average based on simulated autoregressive AR(p) model. We run a simulation using the three above examining method under specific conditions. The main finding is that the optimal k = 4 and then k = 3. Especially, the fourth WMA ARIMA model, fourth EWMA ARIMA model, and third EWMA ARIMA model are the best forecasting models among others, respectively. For all the six real data reveal the similar results of simulation study.