Ordinary least-square (OLS) estimators for a linear model are very sensitive to unusual
values in the design space or outliers among y values. Even one single atypical value may …

Nonparametric regression methods provide an alternative approach to parametric
estimation that requires only weak identification assumptions and thus minimizes the risk of …

Nonparametric modal regression Page 1 The Annals of Statistics 2016, Vol. 44, No. 2, 489–514
DOI: 10.1214/15-AOS1373 © Institute of Mathematical Statistics, 2016 NONPARAMETRIC …

This paper studies the nonparametric modal regression problem systematically from a
statistical learning viewpoint. Originally motivated by pursuing a theoretical understanding of …

Recently, a number of statistical problems have found an unexpected solution by inspecting
them through a 'modal point of view'. These include classical tasks such as clustering or …

Modal regression is a good alternative of the mean regression and likelihood based
methods, because of its robustness and high efficiency. A robust communication-efficient …

In this paper, we consider estimation of the conditional mode of an outcome variable given
regressors. To this end, we propose and analyze a computationally scalable estimator …

We review recent advances in modal regression studies using kernel density estimation.
Modal regression is an alternative approach for investigating the relationship between a …

We in this paper propose a semiparametric partially linear varying coefficient (SPLVC)
modal regression, in which the conditional mode function of the response variable given …

In this paper, under the stationary α-mixing dependent samples, we develop a novel
nonlinear modal regression for time series sequences and establish the consistency and …