… One basic specification in a local regression model is that there is a neighborhood
containing x in which the regression surface is well approximated by a function from a specific …

… however, that characterizes the approach as local regression; we suppose that for each x…
local regression surfaces that is treated in this paper is loess, which is short for local regression …

… of the author’s work in the field of local regression over the past several years. The book is
… These chapters introduce the local regression method in univariate and multivariate settings…

… work in local regression continues at a rapid pace. We review the history of local regression.
We … on the efficacy of using asymptotics to provide guidance for methods of local regression. …

… We do not claim that local regression methods will always have dramatic … local regression
methods, and mean squared error will be very similar. Rather, we argue that local regression …

This paper investigates the large sample properties of local regression distribution estimators,
which include a class of boundary adaptive density estimators as a prime example. First, …

… Results indicate that the local regression coefficients are potentially collinear even if the
underlying exogenous variables in the data generating process are uncorrelated. Based on …

… a weighted local regression procedure … local regression. Further, a close correspondence
is found between the orders of the polynomial to be locally fitted in weighted local regression …

We explore a class of vector smoothers based on local polynomial regression for fitting
nonparametric regression models which have a vector response. The asymptotic bias and …

… An initial bandwidth or window width b is chosen for the first local regression step; we
choose to start with one-quarter the width of the interval spanned by the arguments. This is …