Titlebook: Recent Advances in Linear Models and Related Areas; Essays in Honour of Shalabh,Christian Heumann Book 2008 Physica-Verlag Heidelberg 2008

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Estimating the Number of Clusters in Logistic Regression Clustering by an Information Theoretic Criroach is employed to tackle this problem. An information theoretic criterion for selecting the number of logistic curves is proposed in the sequel and then its asymptotic property is considered..The paper is arranged as follows: In Section 2, some notations are given and an information theoretic cri

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Quasi Score and Corrected Score Estimation in the Polynomial Measurement Error Model,rs in the covariable, cf., e.g., Cheng and Schneeweiss (1998), Cheng and Schneeweiss (2002), Kukush et al. (2005), Kukush and Schneeweiss (2005), Shklyar et al. (2007), some issues concerning the computation of estimators and their asymptotic covariance matrices (ACM) are still open to investigation

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Estimation and Finite Sample Bias and MSE of FGLS Estimator of Paired Data Model,strated in Kloek (1981) and Moulton (1990). When the cross sectional dependence is ignored, the estimated standard errors computed without considering clustering can be understated for OLS estimator, as shown in Cameron and Golotvina (2005). Recent work on treating cross-sectional dependence can be

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Prediction of Finite Population Total in Measurement Error Models,ls with the prediction of finite population total based on regression models with measurement errors. General treatment of regression problems with measurement errors is considered in the pioneering book by Fuller (1987) and Cheng and Van Ness (1999). Later Sprent (1966) proposed methods based on ge

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Local Sensitivity in the Inequality Restricted Linear Model, increasing attention from econometricians and statisticians. Essentially, a diagnostic test ascertains if the model coincides with the assumed data generating process, while sensitivity analysis investigates if it matters at all that the model deviates from what is being assumed. That is, sensitivi

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Boosting Correlation Based Penalization in Generalized Linear Models,arge the estimation of unknown parameters frequently raises problems. Then the interest usually focusses on data driven subset selection of relevant regressors. The sophisticated monitoring equipment which is now routinely used in many data collection processes makes it possible to collect data with

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Simultaneous Prediction Based on Shrinkage Estimator,wever, in various applications, it is utmost important for a practitioner to predict the future values of the response variable. The most common way to tackle with such a problem is the use of Best Linear Unbiased Predictors (BLUP) discussed by Theil (1971), Hendersion (1972) and Judge, Griffiths, H

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Finite Mixtures of Generalized Linear Regression Models,chnique given that they constitute a flexible and-easily extensible model class for (1) approximating general distribution functions in a semi-parametric way and (2) accounting for unobserved heterogeneity. The number of applications has tremendously increased in the last decades as model estimation