Applications of Some Improved Estimators in Linear Regression

The problem of estimation of the regression coefficients under multicollinearity situation for the restricted linear model is discussed. Some improve estimators are considered, including the unrestricted ridge regression estimator (URRE), restricted ridge regression estimator (RRRE), shrinkage restricted ridge regression estimator (SRRRE), preliminary test ridge regression estimator (PTRRE), and restricted Liu estimator (RLIUE). The were compared based on the sampling variance-covariance criterion. The RRRE dominates other ridge estimators when the restriction does or does not hold. A numerical example was provided. The RRRE performed equivalently or better than the RLIUE in the sense of having smaller sampling variance

Robust Predictive Inference for Multivariate Linear Models with Elliptically Contoured Distribution Using Bayesian, Classical and Structural Approaches

Predictive distributions of future response and future regression matrices under multivariate elliptically contoured distributions are discussed. Under the elliptically contoured response assumptions, these are identical to those obtained under matric normal or matric-t errors using structural, Bayesian with improper prior, or classical approaches. This gives inference robustness with respect to departure from the reference case of independent sampling from the matric normal or matric t to multivariate elliptically contoured distributions. The importance of the predictive distribution for skewed elliptical models is indicated; the elliptically contoured distribution, as well as matric t distribution, have significant applications in statistical practices

Applications of some discrete regression models for count data

In this paper we have considered several regression models to fit the count data that encounter in the field of Biometrical, Environmental, Social Sciences and Transportation Engineering. We have fitted Poisson (PO), Negative Binomial (NB), Zero-Inflated Poisson (ZIP) and Zero-Inflated Negative Binomial (ZINB) regression models to run-off-road (ROR) crash data which collected on arterial roads in south region (rural) of Florida State. To compare the performance of these models, we analyzed data with moderate to high percentage of zero counts. Because the variances were almost three times greater than the means, it appeared that both NB and ZINB models performed better than PO and ZIP models for the zero inflated and over dispersed count data

Effect of W, LR, and LM Tests on the Performance of Preliminary Test Ridge Regression Estimators

This paper combines the idea of preliminary test and ridge regression methodology, when it is suspected that the regression coefficients may be restricted to a subspace. The preliminary test ridge regression estimators (PTRRE) based on the Wald (W), Likelihood Ratio (LR) and Lagrangian Multiplier (LM) tests are considered. The bias and the mean square errors (MSE) of the proposed estimators are derived under both null and alternative hypotheses. By studying the MSE criterion, the regions of optimality of the estimators are determined. Under the null hypothesis, the PTRRE based on LM test has the smallest risk followed by the estimators based on LR and W tests. However, the PTRRE based on W test performs the best followed by the LR and LM based estimators when the parameter moves away from the subspace of the restrictions. The conditions of superiority of the proposed estimator for both ridge parameter k and departure parameter (triangle symbol) are provided. Some graphical representations have been presented which support the findings of the paper. Some tables for maximum and minimum guaranteed relative efficiency of the proposed estimators have been provided. These tables allow us to determine the optimum level of significance corresponding to the optimum estimators among proposed estimators. Finally, we concluded that the optimum choice of the level of significance becomes the traditional choice by using the W test for all non-negative ridge parameter, k.Dominance; Lagrangian Multiplier; Likelihood Ratio Test; MSE; Non-central Chisquare and F; Ridge Regression; Superiority; Wald Test.

Modified Ridge Parameters for Seemingly Unrelated Regression Model

In this paper, we modify a number of new biased estimators of seemingly unrelated regression (SUR) parameters which are developed by Alkhamisi and Shukur (2008), AS, when the explanatory variables are affected by multicollinearity. Nine ridge parameters have been modified and compared in terms of the trace mean squared error (TMSE) and (PR) criterion. The results from this extended study are the also compared with those founded by AS. A simulation study has been conducted to compare the performance of the modified ridge parameters. The results showed that under certain conditions the performance of the multivariate ridge regression estimators based on SUR ridge RMSmax is superior to other estimators in terms of TMSE and PR criterion. In large samples and when the collinearity between the explanatory variables is not high the unbiased SUR, estimator produces a smaller TMSEs.Multicollinearity; modified SUR ridge regression; Monte Carlo simulations; TMSE

Empirical Comparison of Some Test Statistics for Testing the Mean of a Poisson Distribution

This paper considers the problem of hypotheses testing of the mean of a Poisson distribution. Accordingly we consider the following test statistics: Wald, WCC, Score (S), FT, VS, RVS, Exact and Bayes test statistics. A simulation study based on both one and two sided alternatives has been conducted to compare the performances of the test statistics. The study suggests that for a large sample size, all proposed test statistics except VCC and FT perform well in the sense of correct type I error rate of the test and power. However, for a small sample size, Score and VS have better type I error rate and power properties than the other test statistics

A Simulation Study on the Size and Power Properties of Some Ridge Regression Tests

Ridge regression techniques have been extensively used to solve the multicollinearity problem for both linear and non-linear regression models since its inception. This paper studied different ridge regression t-type tests of the individual coefficients of a linear regression model. A simulation study has been conducted to evaluate the performance of the proposed tests with respect to their sizes and powers under different settings of the linear regression model. Our simulation results demonstrated that most of the proposed tests have sizes close to the 5% nominal level and all tests except tAKS, tkM2 and tkM9 have considerable gain in powers over the ordinary OLS t-type test. It is also observed that some of the proposed test statistics are performing better than the HK and HKB tests which are proposed some authors

On Some Statistics for Testing the Skewness in a Population: An Empirical Study

The purpose of this paper is to propose some test statistics for testing the skewness parameter of a distribution, not limited to a normal distribution. Since a theoretical comparison is not possible, a simulation study has been conducted to compare the performance of the test statistics. We have compared both parametric methods (classical method with normality assumption) and non-parametric methods (bootstrap in Bias Corrected Standard Method, Efron’s Percentile Method, Hall’s Percentile Method and Bias Corrected Percentile Method). Our simulation results indicate that the power of the tests differ significantly across sample sizes, the choice of alternative hypotheses and methods one choose. When the data are generated from a normal distribution, both classical method and Efron’s Percentile Method can attain a nominal size of 0.05, while other bootstrap methods cannot. However, for a skewed distribution, bootstrap methods show higher power with larger sample sizes whereas the classical method only performs well when the sample size is small

Some Ridge Regression Estimators and Their Performances

The estimation of ridge parameter is an important problem in the ridge regression method, which is widely used to solve multicollinearity problem. A comprehensive study on 28 different available estimators and five proposed ridge estimators, KB1, KB2, KB3, KB4, and KB5, is provided. A simulation study was conducted and selected estimators were compared. Some of selected ridge estimators performed well compared to the ordinary least square (OLS) estimator and some existing popular ridge estimators. One of the proposed estimators, KB3, performed the best. Numerical examples were given

On Some Discrete Distributions and their Applications with Real Life Data

This article reviews some useful discrete models and compares their performance in terms of the high frequency of zeroes, which is observed in many discrete data (e.g., motor crash, earthquake, strike data, etc.). A simulation study is conducted to determine how commonly used discrete models (such as the binomial, Poisson, negative binomial, zero-inflated and zero-truncated models) behave if excess zeroes are present in the data. Results indicate that the negative binomial model and the ZIP model are better able to capture the effect of excess zeroes. Some real-life environmental data are used to illustrate the performance of the proposed models