2013/05/09 by Camila P. E. de Souza, de Souza, Camila P. E., Nancy Heckman +2
Computer Science · Mathematics · #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Methodology (stat.ME) #Statistical Methods and Bayesian Inference #stat.ME
paper · pdf · doi:10.48550/arxiv.1305.2227
The article has one supplementary pdf file (DeSouzaHeckman-supplementA.pdf)
openalex publication_date 2013/05/09 · arxiv created 2013/05/22 · arxiv updated 2013/05/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We propose a methodology to analyze data arising from a curve that, over its domain, switches among J states. We consider a sequence of response variables, where each response y depends on a covariate x according to an unobserved state z. The states form a stochastic process and their possible values are j=1,...,J. If z equals j the expected response of y is one of J unknown smooth functions evaluated at x. We call this model a switching nonparametric regression model. We develop an EM algorithm to estimate the parameters of the latent state process and the functions corresponding to the J states. We also obtain standard errors for the parameter estimates of the state process. We conduct simulation studies to analyze the frequentist properties of our estimates. We also apply the proposed methodology to the well-known motorcycle data set treating the data as coming from more than one simulated accident run with unobserved run labels.