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Bayesian Inference for Analyzing Sports Data by using Bivariate Distribution Models

Title
Bayesian Inference for Analyzing Sports Data by using Bivariate Distribution Models
Author
SabianShahin
Advisor(s)
Seong Wook Kim
Issue Date
2017-08
Publisher
한양대학교
Degree
Doctor
Abstract
It is a common practice to use models based on the bivariate distributions for modeling sports data. These models are used to analyze discrete count data with two dependent variables in the data. In this dissertation we use Markov chain and Monte Carlo techniques to implement on simulated data and we perform real data analysis to demonstrate model fitting performances of our proposed models. We use Poisson regression (BP) and diagonally inflated bivariate Poisson regression (DIBP) models to analyze soccer data and we analyze baseball and basket ball data by using different types of bivariate binomial models (BVB).
URI
http://dcollection.hanyang.ac.kr/jsp/common/DcLoOrgPer.jsp?sItemId=000000102319http://hdl.handle.net/20.500.11754/33531
Appears in Collections:
GRADUATE SCHOOL[S](대학원) > APPLIED MATHEMATICS(응용수학과) > Theses (Master)
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