  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.
