Switching nonparametric regression models for multi‐curve data
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Publication:4960864
DOI10.1002/CJS.11331zbMATH Open1474.62123arXiv1504.02813OpenAlexW2964062613MaRDI QIDQ4960864
Author name not available (Why is that?)
Publication date: 23 April 2020
Published in: (Search for Journal in Brave)
Abstract: We develop and apply an approach for analyzing multi-curve data where each curve is driven by a latent state process. The state at any particular point determines a smooth function, forcing the individual curve to switch from one function to another. Thus each curve follows what we call a switching nonparametric regression model. We develop an EM algorithm to estimate the model parameters. We also obtain standard errors for the parameter estimates of the state process. We consider several types of state processes: independent and identically distributed, independent but depending on a covariate and Markov. Simulation studies show the frequentist properties of our estimates. We apply our methods to a data set of a building's power usage.
Full work available at URL: https://arxiv.org/abs/1504.02813
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