9 research outputs found
Stochastic Restricted Biased Estimators in misspecified regression model with incomplete prior information
In this article, the analysis of misspecification was extended to the
recently introduced stochastic restricted biased estimators when
multicollinearity exists among the explanatory variables. The Stochastic
Restricted Ridge Estimator (SRRE), Stochastic Restricted Almost Unbiased Ridge
Estimator (SRAURE), Stochastic Restricted Liu Estimator (SRLE), Stochastic
Restricted Almost Unbiased Liu Estimator (SRAULE), Stochastic Restricted
Principal Component Regression Estimator (SRPCR), Stochastic Restricted r-k
class estimator (SRrk) and Stochastic Restricted r-d class estimator (SRrd)
were examined in the misspecified regression model due to missing relevant
explanatory variables when incomplete prior information of the regression
coefficients is available. Further, the superiority conditions between
estimators and their respective predictors were obtained in the mean square
error matrix (MSEM) sense. Finally, a numerical example and a Monte Carlo
simulation study were used to illustrate the theoretical findings.Comment: 35 Pages, 6 Figure
Principal Component Preliminary Test Estimator in the Linear Regression Model
A Preliminary Test Estimator is introduced based on Principal Component Regression Estimator defined in the linear regression model when the stochastic restrictions are available in addition to the sample information, and when the explanatory variables are multicollinear. It is further developed as a large sample preliminary test estimator by using Wald (WA), Likelihood Ratio (LR), and Lagrangian Multiplier (LM) tests. Stochastic properties of this estimator based on F test as well as WA, LR, and LM tests are derived, and the performance of the estimator is compared using WA, LR, and LM tests with respect to Mean Square Error Matrix (MSEM). A Monte Carlo simulation is carried out to illustrate the theoretical findings
A comparison study on a new five-parameter generalized Lindley distribution with its sub-models
In recent years, modifications of the classical Lindley distribution have been considered by many authors. In this paper, we introduce a new generalization of the Lindley distribution based on a mixture of exponential and gamma distributions with different mixing proportions and compare its performance with its sub-models. The new distribution accommodates the classical Lindley, Quasi Lindley, Two-parameter Lindley, Shanker, Lindley distribution with location parameter, and Three-parameter Lindley distributions as special cases. Various structural properties of the new distribution are discussed and the size-biased and the lengthbiased are derived. A simulation study is conducted to examine the mean square error for the parameters by means of the method of maximum likelihood. Finally, simulation studies and some real-world data sets are used to illustrate its flexibility in terms of its location, scale and shape parameters
Zero-modified Poisson-Modification of Quasi Lindley distribution and its application
The Poisson-Modification of Quasi Lindley (PMQL) distribution is a newly introduced mixed Poisson distribution for over-dispersed count data. The aim of this article is to introduce the Zero-modified PMQL (ZMPMQL) distribution as an alternative to the PMQL distribution in order to accommodate zero inflation/deflation. The method of obtaining the ZMPMQL distribution jointly with some of its important properties, namely the probability mass and distribution functions, mean, variance, index of dispersion, and quantile function are presented. Furthermore, some of its special cases are discussed. The maximum likelihood (ML) estimation method is used for the unknown parameter estimation. A simulation study is conducted in order to evaluate the asymptotic theory of the ML estimation method and to show the superiority of the ML method over the method of moments estimation. The applicability of the introduced distribution is illustrated by using a real-world data set