Quotation Frühwirth-Schnatter, Sylvia. 2006. Statistical Inference for Highly Parameterized Models for Discrete Valued Data. Joint Statistical Meeting 2006, Seattle, Vereinigte Staaten/USA, 06.08.-10.08. Invited Talk




In this talk, we will be concerned with statistical inference for discretevalued data when modeling is based on complex generalized linear models, such as state-space models for count data or multinomial random- eff ect models. First, we will discuss MCMC estimation for these types of models, which is based on an approximate, but accurate, new mixture auxiliary sampler that introduces two sequences of artificial latent variables. Th is mixture auxiliary sampler leads to a conditionally linear Gaussian model. Next, we will show that auxiliary mixture sampling also is useful for model choice and variable selection.


Press 'enter' for creating the tag

Publication's profile

Status of publication Published
Affiliation WU
Type of publication Paper presented at an academic conference or symposium
Language English
Title Statistical Inference for Highly Parameterized Models for Discrete Valued Data
Event Joint Statistical Meeting 2006
Year 2006
Date 06.08.-10.08
Country United States/USA
Location Seattle
URL http://www.amstat.org/meetings/jsm/2006/PDFs/JSM06AbstractBook.pdf
Invited Talk Y


Frühwirth-Schnatter, Sylvia (Details)
Institute for Statistics and Mathematics IN (Details)
Google Scholar: Search