Creating synthetic discrete-response regression models
Joseph M. Hilbe
Arizona State University
and
Jet Propulsion Laboratory, CalTech
[email protected]
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Abstract. The development and use of synthetic regression models has proven to assist
statisticians in better understanding bias in data, as well as how to best
interpret various statistics associated with a modeling situation. In this
article, I present code that can be easily amended for the creation of
synthetic binomial, count, and categorical response models. Parameters may
be assigned to any number of predictors (which are shown as continuous,
binary, or categorical), negative binomial heterogeneity parameters may be
assigned, and the number of levels or cut points and values may be specified
for ordered and unordered categorical response models. I also demonstrate
how to introduce an offset into synthetic data and how to test synthetic
models using Monte Carlo simulation. Finally, I introduce code for
constructing a synthetic NB2-logit hurdle model.
View all articles by this author:
Joseph M. Hilbe
View all articles with these keywords:
synthetic, pseudorandom, Monte Carlo, simulation, logistic, probit, Poisson, NB1, NB2, NB-C, hurdle, offset, ordered, multinomial
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