MIXED-EFFECTS MODELS FOR MODELINGCARDIAC FUNCTIONS AND TREATMENT EFFECTS Hyejeong JangApril 15, 2011 Mixed-effects model is an efficient tool for analyzing longitudinal data. The randomeffects in mixed-effects model can be used to capture the correlations among repeatedmeasurements within a subject. The time points are not fixed and all available data can beused in mixed-eff...

The object of this p~per is to present a general structure for mixed models which can be used to unify a large number of linear models often encountered in agricultural research work. Researchers often encounter special cases of these models under such diverse conditions that they fail to recognize the links to a common structure. ConSequently, in practice it is not unusual to find very special...

The book “Extending the Linear Model with R” (ELM) [5] first appeared in 2005 and was based on R version 2.2.0. R is updated regularly and so it is natural that some incompatibilities with the current version have been introduced. For most of the chapters, these changes have been minor and have been addressed in the errata and/or subsequent reprintings of the text. However, for chapter 8 and 9,...

Statistical agencies and other institutions collect data under the promise to protect the confidentiality of respondents. When releasing microdata samples, the risk that records can be identified must be assessed. To this aim, a widely adopted approach is to isolate categorical variables key to the identification and analyze multi-way contingency tables of such variables. Common disclosure risk...

Longitudinal data is becoming increasingly common in business, social sciences, and biological sciences due to the advantages it offers over cross-section data in modeling and incorporating heterogeneity among subjects and in being able to make causal inferences from observational data. Parametric models and methods are widely used for analyzing longitudinal data for continuous, discrete, and c...