MODEL SELECTION AND ESTIMATING DEGREES OF FREEDOM IN BAYESIAN LINEAR AND LINEAR MIXED EFFECT MODELS

on the effect of linear & non-linear texts on students comprehension and recalling

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On Generalized Degrees of Freedom and their Application in Linear Mixed Model Selection

The concept of degrees of freedom plays an important role in statistical modeling and is commonly used for measuring model complexity. The nominal degrees of freedom, i.e., the number of unknown parameters, may fail to work in some modeling procedures, in particular linear mixed effects model situations. In this article, we proposed a new definition for generalized degrees of freedom in the con...

Bayesian Inference for Spatial Beta Generalized Linear Mixed Models

In some applications, the response variable assumes values in the unit interval. The standard linear regression model is not appropriate for modelling this type of data because the normality assumption is not met. Alternatively, the beta regression model has been introduced to analyze such observations. A beta distribution represents a flexible density family on (0, 1) interval that covers symm...

Model Selection in Linear Mixed Models

Linear mixed effects models are highly flexible in handling a broad range of data types and are therefore widely used in applications. A key part in the analysis of data is model selection, which often aims to choose a parsimonious model with other desirable properties from a possibly very large set of candidate statistical models. Over the last 5–10 years the literature on model selection in l...

Bayesian variable selection in linear mixed effects models