Analysis of gradual changes in nonparametric regression based on a new optimization method in the non-unique case

Consider a nonparametric regression model with one-dimensional covariates and a continuous regression function.

Assume that the regression function from the left of the covariate support starts equal to zero and then changes at some unknown point.

Our aim is to estimate this gradual change point.

We define and compare various consistent estimators based on a new general optimization method in the case where the aim is to estimate the largest minimization point of some objective function.

We discuss rates of convergence and estimating the regression function based on the gradual change structure.

Bootstrap bias approximation is discussed.

Further applications in a two sample case are considered, where two continuous regression functions first equal and then change at some point of interest.