Switching nonparametric regression models for multi-curve data

Autor:	de Souza, Camila P. E., Heckman, Nancy E., Xu, Helena
Rok vydání:	2015
Předmět:	Statistics - Methodology Statistics - Applications Statistics - Machine Learning
Zdroj:	The Canadian Journal of Statistics 2017
Druh dokumentu:	Working Paper
DOI:	10.1002/cjs.11331
Popis:	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. Comment: 24 pages, 4 figues
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1504.02813 Zobrazit plný text záznamu View this record from Arxiv