Statistics Colloquium: Models for Overdispersed Count Time Series with Excess Zeros
Abstract: Count time series are frequently encountered in biomedical, epidemiological, and public health applications. In principle, such series may exhibit three distinctive features: overdispersion, zero-inflation, and temporal correlation. Devising modeling frameworks that are sufficiently general to accommodate all three of these characteristics poses a daunting challenge. To address this challenge, we discuss the development of frameworks based on both observation-driven and parameter-driven modeling formulations. We illustrate the latter development by presenting a flexible class of dynamic models in the state-space framework. For parameter estimation, we derive a Monte Carlo Expectation-Maximization (MCEM) algorithm, where particle filtering and particle smoothing methods are employed to approximate the high-dimensional integrals in the E-step of the algorithm. To exemplify the proposed methodology, we consider an application based on the evaluation of a participatory ergonomics intervention, which is designed to reduce the incidence of workplace injuries among a group of hospital cleaners. The data consists of aggregated monthly counts of work-related injuries that were reported before and after the intervention.
Bio: Joseph E. (“Joe”) Cavanaugh received a B.S. in Computer Science and a B.S. in Mathematics from Montana Tech in 1986. He received his M.S. in Statistics from Montana State University in 1988, and his Ph.D. in Statistics from the University of California, Davis, in 1993. He is currently a Professor of Biostatistics and the Head of the Department of Biostatistics in the College of Public Health at the University of Iowa, where he has worked since 2003. He holds a secondary appointment in the Department of Statistics and Actuarial Science, and an affiliate appointment in the interdisciplinary doctoral program in Applied Mathematical and Computational Sciences. Dr. Cavanaugh has published over 150 peer-reviewed manuscripts, over 50 of which feature methodological research contributions to statistics and biostatistics. He has published extensively in the areas of model selection and time series analysis. His applied, interdisciplinary research contributions span a wide range of fields, including cardiology, critical care, dentistry, ergonomics, gerontology, health services utilization, hospice care, hospital epidemiology, immunology, infectious diseases, injury prevention, nutrition, oncology, periodontology, pharmacy, psychiatry, psychology, pulmonary care, school violence, and sports medicine. He is an elected Fellow of the American Statistical Association. Dr. Cavanaugh has supervised 17 doctoral dissertations and 34 master’s projects. He has received several awards for teaching and mentoring, including the William T. Kemper Fellowship for Excellence in Teaching at the University of Missouri (in 2000), the College of Public Health Faculty Teaching Award at the University of Iowa (in 2006), and the College of Public Health Faculty Mentor Award at the University of Iowa (in 2019).
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