Dieser Artikel gibt einen Überblick über die Problematik fehlender Daten im Rahmen der statistischen Datenanalyse. Im Prinzip sollte er auch Lesern mit geringem mathematischen und statistischen Wissen dienlich sein und sie mathematisch nicht überfordern. Gegebenenfalls kann über allzu...

Abstract In almost all life or social science researches, subjects are classified into categories by raters, interviewers or observers. Many approaches have been proposed by various authors for analyzing the data or the results obtained from these raters. Symmetry and conditional symmetry models...

Agreement can be regarded as a special case of association and not the other way round. Virtually in all life or social science researches, subjects are being classified into categories by raters, interviewers or observers and both association and agreement measures can be obtained from the...

The Poisson distribution has been widely used for modelling rater agreement using loglinear models. Mostly in all life or social science researches, subjects are being classified into categories by rater, interviewers or observers and most of these tables indicate that the cell counts are...

This paper presents a general loss function under quadratic loss structure and discusses the comparison of risk functions associated with the unbiased least squares and biased Stein-rule estimators of the coefficients in a linear regression model.

Consider the sample of two binary variables X and Y with some missing structure within X or Y. The knowledge about the corresponding values of the observed covariate allows to play through all possible originally' complete data sets. After defining the notation, including some theoretical work,...

When some observations in the sample data are missing, the application of the regression method is considered for the estimation of population mean with and without the use of imputation. The performance properties of the estimators based on the methods of mean imputation, regression imputation...

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.