Estimators of Linear Regression Model and Prediction under Some Assumptions Violation

Stein Type Estimators for Disturbance Variance in Linear Regression Model

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The assumptions of the linear regression model

The paper is prompted by certain apparent deficiences both in the discussion of the regression model in instructional sources for geographers and in the actual empirical application of the model by geographical writers. In the first part of the paper the assumptions of the two regression models, the ‘fixed X’ and the ‘random X’, are outlined in detail, and the relative importance of each of the...

Applications of Some Improved Estimators in Linear Regression

CWCU LMMSE Estimation Under Linear Model Assumptions

The classical unbiasedness condition utilized e.g. by the best linear unbiased estimator (BLUE) is very stringent. By softening the ”global” unbiasedness condition and introducing component-wise conditional unbiasedness conditions instead, the number of constraints limiting the estimator’s performance can in many cases significantly be reduced. In this paper we extend the findings on the compon...

Penalized Estimators in Cox Regression Model

The proportional hazard Cox regression models play a key role in analyzing censored survival data. We use penalized methods in high dimensional scenarios to achieve more efficient models. This article reviews the penalized Cox regression for some frequently used penalty functions. Analysis of medical data namely ”mgus2” confirms the penalized Cox regression performs better than the cox regressi...