Change point estimators by local polynomial fits under a dependence assumption

Local polynomial estimation in partial linear regression models under dependence

A regression model whose regression function is the sum of a linear and a nonparametric component is presented. The design is random and the response and explanatory variables satisfy mixing conditions. A new local polynomial type estimator for the nonparametric component of the model is proposed and its asymptotic normality is obtained. Specifically, this estimator works on a prewhitening tran...

Local Polynomial Regression Estimators in Survey Sampling

Estimation of finite population totals in the presence of auxiliary information is considered. A class of estimators based on local polynomial regression is proposed. Like generalized regression estimators, these estimators are weighted linear combinations of study variables, in which the weights are calibrated to known control totals, but the assumptions on the superpopulation model are consid...

Baseline Survival Function Estimators under Proportional Hazards Assumption

Cox (1972) proposed the partial likelihood technique to estimate the risk coefficients of proportional hazards regression model without specify the baseline hazards function. Once the risk coefficients β are estimated, we may interest in estimating the corresponding baseline survival function. Therefore, Breslow (1972) and Kalbfleisch & Prentice (1973) provided two different procedures as the b...

Normal Approximation under Local Dependence by Louis

Matrix Approximation under Local Low-Rank Assumption

Matrix approximation is a common tool in machine learning for building accurate prediction models for recommendation systems, text mining, and computer vision. A prevalent assumption in constructing matrix approximations is that the partially observed matrix is of low-rank. We propose a new matrix approximation model where we assume instead that the matrix is only locally of low-rank, leading t...