Application of hidden Markov models on residuals: an example using Canadian traffic accident data.
Laverty, Kelly, Rotton, and Flynn conducted a regression analysis in 1992 on 9 years of automobile accidents in Saskatchewan (a total of 200,545 accidents) to find a small linear trend, season effects, holiday, and day of the week effects. The application of a hidden Markov model to the residuals of this analysis uncovered two states which are likely to be related to the weather. These states can be described as low volatility' and 'high volatility'. The 'low volatility' state involves low variability compared to the 'high volatility' state (occurring during the colder months) during which the largest numbers of accidents occur. It is suggested that hidden Markov models are a useful method for uncovering hidden, underlying states in social science and health-related data.
Department of Educational Psychology and Special Education, University of Saskatchewan, Saskatoon, Canada.
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Laverty
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Miket
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Kelly
D000063:Accidents, Traffic / Q000706:statistics & numerical data* / Q000639:trends; D015984:Causality; D003430:Cross-Sectional Studies; D006801:Humans; D008390:Markov Chains; D012044:Regression Analysis; D012525:Saskatchewan; D012621:Seasons; D014887:Weather