Posterior mean and variance approximation for regression and time series problems

Details Posterior mean and variance approximation for regression and time series problems Posterior mean and variance approximation for regression and time series problems

2008-02-01

arxiv.org/abs/0802.0213v1

Statistics (2008), 42, pp. 329-350.

Statistics

Methodology

25 pages, 2 figures, 2 tables

Scientific paper

This paper develops a methodology for approximating the posterior first two moments of the posterior distribution in Bayesian inference. Partially specified probability models, which are defined only by specifying means and variances, are constructed based upon second-order conditional independence, in order to facilitate posterior updating and prediction of required distributional quantities. Such models are formulated particularly for multivariate regression and time series analysis with unknown observational variance-covariance components. The similarities and differences of these models with the Bayes linear approach are established. Several subclasses of important models, including regression and time series models with errors following multivariate $t$, inverted multivariate $t$ and Wishart distributions, are discussed in detail. Two numerical examples consisting of simulated data and of US investment and change in inventory data illustrate the proposed methodology.

Affiliated with

Also associated with

No associations

Rating

Posterior mean and variance approximation for regression and time series problems does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Posterior mean and variance approximation for regression and time series problems, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Posterior mean and variance approximation for regression and time series problems will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-189382