Application of the Conway-Maxwell-Poisson Hidden Markov models for analyzing traffic accident

• 발행기관: 한국자료분석학회
• 저자명: Hee-Young Kim Hyo-Jin Bae Su-Hyeon Kim
• 간행물 정보: 『한국자료분석학회 학술대회자료집』2022년 하계학술대회 발표집, 115~115쪽, 전체 1쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2022.07.07

영문 초록

This paper documents the application of a hidden Markov model with Conway- Maxwell-Poisson(CMP) state-dependent distribution. The CMP distribution originally delevoped by Conway and Maxwell(1962) for modeling ques and service rates. And recently CMP has been re-introduced by Shmueli et al.(2005) for analyzing count data subjected to over- and under-dispersion. Over the past half centry, Hidden Markov Models(HMMs) have become increasingly popular tools for modeling time series data where, at each point in time, a hidden state process selects among a finite set of possible distributions for the observations. We model the distributions of the traffic accident covearing the period 2020, compring T=366 ovservations in total, using CMP-HMM and Poisson-HMM, which allows for overdispersion and serial dependence. Our results indicate two-state CMP-HMM is improvement on the two-state Poisson-HMM by AIC and BIC. Also we present the full array of the conditional distributions, i.e., the distribution of 𝑋ₜ conditioned on all the other observations at all times other than 𝑡.

키워드

Count data Time series modeling Conway-Maxcwell-Poisson distribution Hidden Markov model