Last week in non-life insurance course, we’ve seen the theory of the Generalized Linear Models, emphasizing the two important components the link function (which is actually the key component in predictive modeling) the distribution, or the variance function Just to illustrate, consider my favorite dataset ­lin.mod = lm(dist\textasciitildespeed,data=cars) A linear model means here where the residuals are assumed to be centered, independent, and with identical variance. If we visualize that linear regression, we usually see something like that The idea here (in GLMs) is to [...]
@misc{_glm_????,
title = {{GLM}, non-linearity and heteroscedasticity},
url = {http://www.r-bloggers.com/glm-non-linearity-and-heteroscedasticity/},
abstract = {Last week in non-life insurance course, we’ve seen the theory of the Generalized Linear Models, emphasizing the two important components the link function (which is actually the key component in predictive modeling) the distribution, or the variance function Just to illustrate, consider my favorite dataset ­lin.mod = lm(dist{\textasciitilde}speed,data=cars) A linear model means here where the residuals are assumed to be centered, independent, and with identical variance. If we visualize that linear regression, we usually see something like that The idea here (in GLMs) is to [...]},
urldate = {2013-10-31TZ},
journal = {R-bloggers}
}