momMix

<code>momMix</code> averages the moments of a normal mixture model over MCMC draws.

multivariate

Covers many important models used
in marketing and micro-econometrics applications.
The package includes:
Bayes Regression (univariate or multivariate dep var),
Bayes Seemingly Unrelated Regression (SUR),
Binary and Ordinal Probit,
Multinomial Logit (MNL) and Multinomial Probit (MNP),
Multivariate Probit,
Negative Binomial (Poisson) Regression,
Multivariate Mixtures of Normals (including clustering),
Dirichlet Process Prior Density Estimation with normal base,
Hierarchical Linear Models with normal prior and covariates,
Hierarchical Linear Models with a mixture of normals prior and covariates,
Hierarchical Multinomial Logits with a mixture of normals prior
and covariates,
Hierarchical Multinomial Logits with a Dirichlet Process prior and covariates,
Hierarchical Negative Binomial Regression Models,
Bayesian analysis of choice-based conjoint data,
Bayesian treatment of linear instrumental variables models,
Analysis of Multivariate Ordinal survey data with scale
usage heterogeneity (as in Rossi et al, JASA (01)),
Bayesian Analysis of Aggregate Random Coefficient Logit Models as in BLP (see
Jiang, Manchanda, Rossi 2009)
For further reference, consult our book, Bayesian Statistics and
Marketing by Rossi, Allenby and McCulloch (Wiley first edition 2005 and second forthcoming) and Bayesian Non- and Semi-Parametric
Methods and Applications (Princeton U Press 2014).

Peter Rossi

bayesm

Bayesian Inference for Marketing/Micro-Econometrics

momMix function

<dl> <dt>probdraw</dt>
<dd>\(R x ncomp\) list of draws of mixture probs</dd> <dt>compdraw</dt>
<dd>list of length \(R\) of draws of mixture component moments</dd></dl>

Arguments

This routine is a utility routine that does not check the input arguments for proper dimensions and type.

Warning

Peter Rossi, Anderson School, UCLA, <a href="/link/perossichi%40gmail.com?package=bayesm&version=3.1-6" data-mini-rdoc="bayesm::perossichi@gmail.com">perossichi@gmail.com</a>.

Author

Compute Posterior Expectation of Normal Mixture Model Moments — momMix

<dl>

 <dt>probdraw</dt>
<dd>\(R x ncomp\) list of draws of mixture probs</dd>

 <dt>compdraw</dt>
<dd>list of length \(R\) of draws of mixture component moments</dd>

</dl>

Peter Rossi, Anderson School, UCLA, <a href='mailto:perossichi@gmail.com'>perossichi@gmail.com</a>.

`R`	is the number of MCMC draws in argument list above.
`ncomp`	is the number of mixture components fitted.
`compdraw`	is a list of lists of lists with mixture components.
`compdraw[[i]]`	is \(i\)th draw.
`compdraw[[i]][[j]][[1]]`	is the mean parameter vector for the \(j\)th component, \(i\)th MCMC draw.
`compdraw[[i]][[j]][[2]]`	is the UL decomposition of \(\Sigma^{-1}\) for the \(j\)th component, \(i\)th MCMC draw

momMix: Compute Posterior Expectation of Normal Mixture Model Moments

Description

Usage

Value

Arguments

Warning

Author

Details

References

See Also