Consequences of Departure from Normality on the Properties of Calibration Estimators
نویسنده
چکیده
This paper considers the classical and inverse calibration estimators and discusses the consequences of departure from normality of errors on their bias and mean squared error properties when the errors in calibration process are small.
منابع مشابه
Impact of Departure from Normality on theE
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 coeecients 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 propertie...
متن کاملImpact 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 obser vations on an explanatory variable are missing and presents three least squares estimators for the regression coe cients 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 ...
متن کاملFractional Poisson Process
For almost two centuries, Poisson process with memoryless property of corresponding exponential distribution served as the simplest, and yet one of the most important stochastic models. On the other hand, there are many processes that exhibit long memory (e.g., network traffic and other complex systems). It would be useful if one could generalize the standard Poisson process to include these p...
متن کاملCalibration Weighting to Compensate for Extreme Values, Non-response and Non-coverage in Labor Force Survey
Frame imperfection, non-response and unequal selection probabilities always affect survey results. In order to compensate for the effects of these problems, Devill and Särndal (1992) introduced a family of estimators called calibration estimators. In these estimators we look for weights that have minimum distance with design weights based on a distance function and satisfy calibration equa...
متن کاملAsymptotic Efficiencies of the MLE Based on Bivariate Record Values from Bivariate Normal Distribution
Abstract. Maximum likelihood (ML) estimation based on bivariate record data is considered as the general inference problem. Assume that the process of observing k records is repeated m times, independently. The asymptotic properties including consistency and asymptotic normality of the Maximum Likelihood (ML) estimates of parameters of the underlying distribution is then established, when m is ...
متن کامل