Impact of Departure from Normality on the Efficiency of Estimating Regression Coefficients when Some Observations are Missing

This article considers a linear regression model in which some observations on an explanatory variable are missing, and presents three least squares estimators for the regression coefficients vector. One estimator uses complete observations alone while the other two estimators utilize repaired data with nonstochastic and stochastic imputed values for the missing observations. Asymptotic properties of these estimators based on small disturbance asymptotic theory are derived and the impact of departure from normality of disturbances is examined. 1 Introduction During the process of data collection, we often encounter situations where some observations cannot be recorded for one reason or the other. Such instances occur quite frequently in mail surveys, opinion surveys, crop surveys, socioeconomic enquiries and planned experimentation in biological, industrial and medical sciences. Consequently, the traditional statistical analysis cannot be conducted due to some missing observations. No..

Estimation of regression coefficients subject to exact linear restrictions when some observations are missing and quadratic error balanced loss function is used

Balanced loss function, exact restrictions, missing observations, 62J05,

Pseudo-minimax linear and mixed regression estimation of regression coefficients when prior estimates are available

When prior estimates of regression coefficients along with their standard errors or their variance-covariance matrix are available, they can be incorporated into the estimation procedure through minimax linear and mixed regression approaches. It is demonstrated that the mixed regression approach provides more efficient estimators, at least asymptotically, in comparison to the minimax linear approach with respect to the criterion of variance-covariance matrix.Linear regression Mixed model