Overview of the Loglinear Variance Model | JMP 13.2

JMP Support 800.450.0135 (US)

Documentation Feedback
Your feedback is important to us. us any comments about our documentation.

Fitting Linear Models • Loglinear Variance Models • Overview of the Loglinear Variance Model

•

Overview of the Loglinear Variance Model

The loglinear-variance model (Harvey 1976, Cook and Weisberg 1983, Aitken 1987, Carroll and Ruppert 1988) provides a way to model the variance simply through a linear model. In addition to having regressor terms to model the mean response, there are regressor terms in a linear model to model the log of the variance:

mean model: E(y) = Xβ

variance model: log(Variance(y)) = Z λ,

or equivalently

Variance(y) = exp(Z λ)

where the columns of X are the regressors for the mean of the response, and the columns of Z are the regressors for the variance of the response. The regular linear model parameters are represented by β, and λ represents the parameters of the variance model.

Log-linear variance models are estimated using REML.

A dispersion or log-variance effect can model changes in the variance of the response. This is implemented in the Fit Model platform by a fitting personality called the Loglinear Variance personality.

•

Help created on 9/19/2017