Errata—Optimal Sampling Strategies for Statistical Models with Discrete Dependent Variables

What do instrumental variable models deliver with discrete dependent variables?

on some bayesian statistical models in actuarial science with emphasis on claim count

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Gibbs sampling for parsimonious Markov models with latent variables

Parsimonious Markov models have been recently developed as a generalization of variable order Markov models. Many practical applications involve a setting with latent variables, with a common example being mixture models. Here, we propose a Bayesian model averaging approach for learning mixtures of parsimonious Markov models that is based on Gibbs sampling. The challenging problem is sampling o...

Bernoulli Regression Models: Re-examining Statistical Models with Binary Dependent Variables

The classical approach for specifying statistical models with binary dependent variables in econometrics using latent variables or threshold models can leave the model misspecified, resulting in biased and inconsistent estimates as well as erroneous inferences. Furthermore, methods for trying to alleviate such problems, such as univariate generalized linear models, have not provided an adequate...

Surrogate Models for Mixed Discrete-Continuous Variables

Large-scale computational models have become common tools for analyzing complex manmade systems. However, when coupled with optimization or uncertainty quantification methods in order to conduct extensive model exploration and analysis, the computational expense quickly becomes intractable. Furthermore, these models may have both continuous and discrete parameters. One common approach to mitiga...