Linear mixed model (LMM) methodology is a powerful technology to analyze models containing both the fixed and random effects. The model was first proposed to estimate genetic parameters for unbalanced ...

The Canadian Journal of Statistics / La Revue Canadienne de Statistique, Vol. 24, No. 2 (Jun., 1996), pp. 177-192 (16 pages) Liang and Zeger (1986) introduced a class of estimating equations that ...

Bayesian variable selection has gained much empirical success recently in a variety of applications when the number K of explanatory variables $(x_{1},\ldots ,x_{K ...

Keywords: Statistical analyses. Regression models. Post-earthquake ignitions. Data analyses. California. Ground shaking. Generalized linear mixed models. Goodness-of-fit analyses. Census tracts. Fire ...

This section provides an overview of a likelihood-based approach to general linear mixed models. This approach simplifies and unifies many common statistical analyses, including those involving ...

You construct a generalized linear model by deciding on response and explanatory variables for your data and choosing an appropriate link function and response probability distribution. Some examples ...

This course is compulsory on the MSc in Statistics (Social Statistics) and MSc in Statistics (Social Statistics) (Research). This course is available on the MPhil/PhD in Statistics, MSc in Data ...

Linear models, generalized linear models, and nonlinear models are examples of parametric regression models because we know the function that describes the relationship between the response and ...

Researchers from many fields can benefit from applied knowledge of general linear models. This class of models includes the t-test (paired and two sample), regression, ANOVA, and ANCOVA. Like all ...