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aic
2012 Feb 22
3
gamlss results for EXP and LNO seem to have reversed AIC scores
Hi,
I'm a bit puzzled by the gamlss fitting of exponential and lognormal data.
Gamlss seems to think that exponentially distributed data fits better with a
lognormal distribution, and vice versa.
For example,
X <- rexp(1000)
Gexp <- gamlss(X~1,family=EXP) # X~1 is X tilde 1
GAMLSS-RS iteration 1: Global Deviance = 2037.825
GAMLSS-RS iteration 2: Global Deviance = 2037.825
Glno
2013 Oct 27
1
R-help Digest, Vol 128, Issue 29
...r (e.g.
standard deviastion) [and also heterogeity in skewness and kurtosis
parameters].of the response variable distribution.
For parametric models a generalized likelihood ratio test can be used to
test whether the heterogeity is needed.
Alternatively a generalized Akaike information criterion (GAIC) can be used
to compare models.
Robert Rigby
On Date: Sat, 26 Oct 2013 23:57:51 -0400 (EDT Collin Lynch <
collinl@cs.pitt.edu> wrote
Subject: [R] Heteroscedasticity and mgcv.
I have a two part question one about statistical theory and the other
about implementations in R. Thank you f...
2013 Oct 27
2
Heteroscedasticity and mgcv.
I have a two part question one about statistical theory and the other
about implementations in R. Thank you for all help in advance.
(1) Am I correct in understanding that Heteroscedasticity is a problem for
Generalized Additive Models as it is for standard linear models? I am
asking particularly about the GAMs as implemented in the mgcv package.
Based upon my online search it seems that some