mixDenBi

<code>mixDenBi</code> computes the implied bivariate marginal density from a mixture of normals with specified mixture probabilities and component parameters.

multivariate

models

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

mixDenBi function

<dl> <dt>i</dt>
<dd>index of first variable</dd> <dt>j</dt>
<dd>index of second variable</dd> <dt>xi</dt>
<dd>grid of values of first variable</dd> <dt>xj</dt>
<dd>grid of values of second variable</dd> <dt>pvec</dt>
<dd>normal mixture probabilities</dd> <dt>comps</dt>
<dd>list of lists of components</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 Bivariate Marginal Density for a Normal Mixture — mixDenBi

<dl>

 <dt>i</dt>
<dd>index of first variable</dd>

 <dt>j</dt>
<dd>index of second variable</dd>

 <dt>xi</dt>
<dd>grid of values of first variable</dd>

 <dt>xj</dt>
<dd>grid of values of second variable</dd>

 <dt>pvec</dt>
<dd>normal mixture probabilities</dd>

 <dt>comps</dt>
<dd>list of lists of components</dd>

</dl>

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

`length(comps)`	is the number of mixture components
`comps[[j]]`	is a list of parameters of the \(j\)th component
`comps[[j]]$mu`	is mean vector
`comps[[j]]$rooti`	is the UL decomp of \(\Sigma^{-1}\)

mixDenBi: Compute Bivariate Marginal Density for a Normal Mixture

Description

Usage

Value

Arguments

Warning

Author

Details

References

See Also