Semi-parametric Bayesian Inference for Multi-Season Baseball Data

Simple item page
dc.contributor.authorQuintana, Fernando A.
dc.contributor.authorMueller, Peter
dc.contributor.authorRosner, Gary L.
dc.contributor.authorMunsell, Mark
dc.date.accessioned2024-01-10T14:21:37Z
dc.date.available2024-01-10T14:21:37Z
dc.date.issued2008
dc.description.abstractWe analyze complete sequences of successes (hits, walks, and sacrifices) for a group of players from the American and National Leagues, collected over 4 seasons. The goal is to describe how players' performance vary from season to season. In particular, we wish to assess and compare the effect of available occasion-specific covariates over seasons. The data are binary sequences for each player and each season. We model dependence in the binary sequence by an autoregressive logistic model. The model includes lagged terms up to a fixed order. For each player and season we introduce a different set of autologistic regression coefficients, i.e., the regression coefficients are random effects that are specific of each season and player. We use a nonparametric approach to define a random effects distribution. The nonparametric model is defined as a mixture with a Dirichlet process prior for the mixing measure. The described model is justified by a representation theorem for order-k exchangeable sequences. Besides the repeated measurements for each season and player, multiple seasons within a given player define an additional level of repeated measurements. We introduce dependence at this level of repeated measurements by relating the season-specific random effects vectors in an autoregressive fashion. We ultimately conclude that while some covariates like the ERA of the opposing pitcher are always relevant, others like an indicator for the game being into the seventh inning may be significant only for certain season, and some others, like the score of the game, can safely be ignored.
dc.description.funderFONDECYT
dc.description.funderU.S. National Cancer Institute
dc.description.funderNATIONAL CANCER INSTITUTE
dc.fechaingreso.objetodigital2024-05-15
dc.format.extent22 páginas
dc.fuente.origenWOS
dc.identifier.doi10.1214/08-BA312
dc.identifier.eissn1936-0975
dc.identifier.issn1931-6690
dc.identifier.pubmedidMEDLINE:21909346
dc.identifier.urihttps://doi.org/10.1214/08-BA312
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/79732
dc.identifier.wosidWOS:000207454900007
dc.information.autorucMatemática;Quintana FA;S/I;100343
dc.issue.numero2
dc.language.isoen
dc.nota.accesocontenido completo
dc.pagina.final338
dc.pagina.inicio317
dc.publisherINT SOC BAYESIAN ANALYSIS
dc.revistaBAYESIAN ANALYSIS
dc.rightsacceso abierto
dc.subjectDirichlet Process
dc.subjectPartial Exchangeability
dc.subjectSemiparametric Random Effects
dc.subject.ods03 Good Health and Well-being
dc.subject.odspa03 Salud y bienestar
dc.titleSemi-parametric Bayesian Inference for Multi-Season Baseball Data
dc.typeartículo
dc.volumen3
sipa.codpersvinculados100343
sipa.indexWOS
sipa.indexScopus
sipa.trazabilidadCarga SIPA;09-01-2024
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Bayesian Analysis - 2008 - Semi-parametric Bayesian Inference for Multi-Season Baseball Data.pdf
Size:
564.03 KB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Artículos de revistas