Asymptotically risk-efficient sequential point estimation of regular functionals of distribution functions based on U-statistics is considered under appropriate regularity conditions. Some auxiliary results on U-statistics are also considered in this context. fu~S Subject Classification: 62L12, 62LlS, 62G99.

We consider two problems concerning locating change points in a linear regression model. One involves jump discontinuities (change-point) in a regression model and the other involves regression lines connected at unknown points. We compare four methods for estimating single or multiple change points in a regression model, when both the error variance and regression coefficients change simultane...

The goal of the change-point detection is to discover changes time series distribution. One state art approaches based on direct density ratio estimation. In this work, we show how existing algorithms can be generalized using various binary classification and regression models. particular, that Gradient Boosting over Decision Trees Neural Networks used for purpose. are tested several synthetic ...

The problem of estimation of change-points in a sequence of Poisson random variables is approached by allowing the change-point to range over the continuous time interval (0; T). A maximum-likelihood point estimator is derived, along with a Bayesian-based interval estimator. Simulation studies connrm that the true coverage probability is close to the nominal one for several locations of the cha...

The paper develops a comprehensive asymptotic theory for the estimation of a change– point in the mean function of functional observations. We consider both the case of a constant change size, and the case of a change whose size approaches zero, as the sample size tends to infinity. We show how the limit distribution of a suitably defined change– point estimator depends on the size and location...