In the linear regression models with AR (1) error structure when collinearity exists, stochastic linear restrictions or modifications of biased estimators (including Liu estimators) can be used to reduce the estimated variance of the regression coefficients estimates. In this paper, the combination of the biased Liu estimator and stochastic linear restrictions estimator is considered to overcom...

In this paper, we propose a new ridge-type estimator called the new mixed ridge estimator (NMRE) by unifying the sample and prior information in linear measurement error model with additional stochastic linear restrictions. The new estimator is a generalization of the mixed estimator (ME) and ridge estimator (RE). The performances of this new estimator and mixed ridge estimator (MRE) against th...

the aim of this paper is to propose some diagnostic methods in linear ridge measurement error models with stochastic linear restrictions using the corrected likelihood. based on the bias-corrected estimation of model parameters, diagnostic measures are developed to identify outlying and influential observations. in addition, we derive the corrected score test statistic for outliers detection ba...

The aim of this paper is to propose some diagnostic methods in linear ridge measurement error models with stochastic linear restrictions using the corrected likelihood. Based on the bias-corrected estimation of model parameters, diagnostic measures are developed to identify outlying and influential observations. In addition, we derive the corrected score test statistic for outliers detection ba...

In this paper we study the estimator of the Partially linear Varying-coefficient Errors-inVariable model with stochastic linear restrictions. We present a mixed Profile least squares estimator when the covariates in the linear part are measured with additive error and some additional stochastic linear restrictions on the parametric component are available.

Outliers and influential observations have important effects on the regression analysis. The goal of this paper is to extend the mean-shift model for detecting outliers in case of ridge regression model in the presence of stochastic linear restrictions when the error terms follow by an autoregressive AR(1) process. Furthermore, extensions of measures for diagnosing influential observations are ...

for vectors x, y ∈ rn, it is said that x is left matrix majorizedby y if for some row stochastic matrix r; x = ry. the relationx ∼` y, is defined as follows: x ∼` y if and only if x is leftmatrix majorized by y and y is left matrix majorized by x. alinear operator t : rp → rn is said to be a linear preserver ofa given relation ≺ if x ≺ y on rp implies that t x ≺ ty onrn. the linear preservers o...

We consider totally unimodular (TU) stochastic programs, that is, two-stage stochastic programs whose extensive-form constraint matrix is TU. We generalize the notion of total unimodularity to apply to sets of matrices and provide properties of such sets. We provide several sufficient conditions on stochastic programs to be TU. When solving TU stochastic problems using the L-shaped method, it i...