A generalized maximum entropy estimator is developed for the linear simultaneous equations model. Monte Carlo sampling experiments are used to evaluate the estimator’s performance in small and medium sized samples, suggesting contexts in which the current generalized maximum entropy estimator is superior in mean square error to two and three stage least squares. Analytical results are provided ...

This note calls attention to a variant formulation of the least-squares adjustment procedure [1] that should prove increasingly attractive as electronic dataprocessing equipment becomes ever more widely available. It indicates that the statistician need not concern himself with the two rituals featured in traditional textbook presentations—namely, the construction and solution of the convention...

This paper explores the potential of several popular equalization techniques and proposes new approaches to overcome their disadvantages. Such as the conventional least-mean-square (LMS ) algorithm, the recursive least-squares ( RLS ) algorithm, the filtered-X LMS algorithm and their development. An H 2 optimal initialization has been proposed to overcome the slow convergence problem while keep...

Over the past decade, numerous studies examined the critical factors that affect the learning outcomes and student satisfaction in asynchronous online learning courses including learning management systems (LMS). This research focuses on the effects of learner’s psychological variables (e.g., self-efficacy) and psychological learning process (e.g., self-regulated learning management) on student...

In this paper, the effect of misspecification due to omission of relevant variables on the dominance of the r −(k, d) class estimator proposed by Özkale (2012), over the ordinary least squares (OLS) estimator and some other competing estimators when some of the regressors in the linear regression model are correlated, have been studied with respect to the mean squared error criterion. A simulat...

A nonlinear regression model with correlated, normally distributed errors is investigated. The bias and the mean square error matrix of the approximate least squares estimator of regression parameters are derived and their limit properties are studied.

Ridge regression is often favored in the analysis of ill-conditioned systems. A canonical form identifies regions in the parameter space where Ordinary Least Squares (OLS) is problematic. The objectives are two-fold: To reexamine the view that ill-conditioning necessarily degrades essentials of OLS; and to reassess ranges of the ridge parameter k where ridge is efficient in mean squared error (...