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Full-Text Articles in Physical Sciences and Mathematics
Bayesian Change Point Detection In Segmented Multi-Group Autoregressive Moving-Average Data For The Study Of Covid-19 In Wisconsin, Russell Latterman
Bayesian Change Point Detection In Segmented Multi-Group Autoregressive Moving-Average Data For The Study Of Covid-19 In Wisconsin, Russell Latterman
Theses and Dissertations
Changepoint detection involves the discovery of abrupt fluctuations in population dynamics over time. We take a Bayesian approach to estimating points in time at which the parameters of an autoregressive moving average (ARMA) change, applying a Markov chain Monte Carlo method. We specifically assume that data may originate from one of two groups. We provide estimates of all multi-group parameters of a model of this form for both simulated and real-world data sets. We include a provision to resolve the problem of confounding ARMA parameter estimates and variance of segment data. We apply our model to identify points in time …
Robust Latent Ability Estimation Based On Item Response Information And Model Fit, Hotaka Maeda
Robust Latent Ability Estimation Based On Item Response Information And Model Fit, Hotaka Maeda
Theses and Dissertations
Aberrant testing behaviors may result in inaccurate person trait estimation. To counter its effects, a new robust ability estimation procedure called downweighting of aberrant responses estimation (DARE) is developed. This procedure downweights both uninformative items and model-misfitting response patterns. The purpose of this study is to present DARE and to evaluate its performance against other robust methods, including biweight (Mislevy & Bock, 1982) and biweight-MAP (BMAP; Maeda & Zhang, 2017b). The traditional maximum likelihood (MLE) and maximum a-posteriori (MAP) methods are also included as baseline methods. A Monte Carlo simulation is conducted with the design variables being test length, type …