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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On the Identification of Trend and Correlation in Temporal and Spatial Regression,e. In particular, it can become difficult to separate trend from correlation. We first exemplify this using a simple time series setting in Section 2. In Section 3 we move on to the corresponding spatial situation, which arises in geostatistics. Section 4 brie.y points out extensions to the general

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Estimation and Finite Sample Bias and MSE of FGLS Estimator of Paired Data Model,el. We consider a similar model to theirs and give a tractable FGLS estimator and investigate its finite sample bias and mean square error (MSE). Our estimator uses OLS and fixed effect (FE) residuals to estimate the covariance matrix of composite errors. Under the assumption of normal disturbances,

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Prediction of Finite Population Total in Measurement Error Models,ictors for finite population with errors in variable models. Recently, Kim and Saleh (2002, 2003, 2005) pioneered the application of preliminary test and shrinkage estimation methodology in measurement error models. Recent book of Saleh (2006) presents an overview on the theory of preliminary test a

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Local Sensitivity in the Inequality Restricted Linear Model, of the parameters not changing much after fitting the model with a more general covariance structure. However, the . and . tests based on the OLS residuals are sensitive to covariance misspecification in the sense that a small stepping away from white noise disturbances is likely to cause a substan

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Simultaneous Prediction Based on Shrinkage Estimator,on in an autoregressive model. Zellner and Hong (1989) used Bayesian shrinkage rules to forecast international growth rate. Hill and Fomby (1992) analyzed the performance of various improved estimators under an out-of-sample prediction mean square error criterion. Gotway and Cressie (1993) considere

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,Regression Calibration for Cox Regression Under Heteroscedastic Measurement Error — Determining Rislinear regression models, while, among others, Strobl, Boulesteix and Augustin (2007) and Svejdar et al. (2007) are concerned with classification under missing data..The paper is organized as follows: The next section describes our modeling of the replication data. Section 3 adapts the idea of regre