Comparative evaluation of various frequentist and Bayesian non-homogeneous Poisson counting models

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• 作者：Marco Grzegorczyk ; Mahdi Shafiee Kamalabad
• 关键词：Non ; homogeneous count data ; Mixture model ; Changepoint model ; Hidden Markov model ; Bayesian paradigm ; Frequentist paradigm ; New York City Taxi data
• 刊名：Computational Statistics
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：32
• 期：1
• 页码：1-33
• 全文大小：1150KB
• 刊物类别：Mathematics and Statistics
• 刊物主题：Statistics, general; Probability and Statistics in Computer Science; Probability Theory and Stochastic Processes; Economic Theory/Quantitative Economics/Mathematical Methods;
• 出版者：Springer Berlin Heidelberg
• ISSN：1613-9658
• 卷排序：32

文摘

In this paper a comparative evaluation study on popular non-homogeneous Poisson models for count data is performed. For the study the standard homogeneous Poisson model (HOM) and three non-homogeneous variants, namely a Poisson changepoint model (CPS), a Poisson free mixture model (MIX), and a Poisson hidden Markov model (HMM) are implemented in both conceptual frameworks: a frequentist and a Bayesian framework. This yields eight models in total, and the goal of the presented study is to shed some light onto their relative merits and shortcomings. The first major objective is to cross-compare the performances of the four models (HOM, CPS, MIX and HMM) independently for both modelling frameworks (Bayesian and frequentist). Subsequently, a pairwise comparison between the four Bayesian and the four frequentist models is performed to elucidate to which extent the results of the two paradigms (‘Bayesian vs. frequentist’) differ. The evaluation study is performed on various synthetic Poisson data sets as well as on real-world taxi pick-up counts, extracted from the recently published New York City Taxi database.