We propose an extrinsic regression framework for modeling data with manifold valued
responses and Euclidean predictors. Regression with manifold responses has wide …

Effective utilization of signals collected by distributed sensor networks is crucial for tracking
degradation and forecasting the remaining useful life (RUL) of rolling bearings. The phase …

High-dimensional data that evolve dynamically feature predominantly in the modern data
era. As a partial response to this, recent years have seen increasing emphasis to address …

In this paper we consider a heteroscedastic transformation model of the form Λϑ (Y)= m (X)+
σ (X) ε, where Λϑ belongs to a parametric family of monotone transformations, m (·) and σ (·) …

The asymptotic bias and variance of a general class of local polynomial estimators of M-
regression functions are studied over the whole compact support of the multivariate …

In this paper, for a wide class of nonparametric regression models, new local linear kernel
estimators are proposed that are uniformly consistent under close-to-minimal and visual …

The consistency of classical local linear kernel estimators in nonparametric regression is
proved under constraints on design elements (regressors) weaker than those known earlier …

Nonparametric estimators of a regression function with circular response and R^ d R d-
valued predictor are considered in this work. Local polynomial estimators are proposed and …

We propose two methods based on the functional principal component analysis (FPCA) to
estimate smooth derivatives for a sample of observed curves with a multidimensional …

An instrumental variable approach to the nonparametric estimation of binary response
models with endogenous variables is presented. Identification is achieved via a reduced …