activity
20052019
most citedLocal linear spatial regression

111 citations · 514 across the 9 of their papers we have counts for

collaborators
Showing math.STShow all

7 papers · 1 filter

math.ST2015★ 22 cited

Local bilinear multiple-output quantile/depth regression

Marc Hallin, Zudi Lu, Davy Paindaveine +1

A new quantile regression concept, based on a directional version of Koenker and Bassett's traditional single-output one, has been introduced in [Ann. Statist. (2010) 38 635-669] f…

math.ST2014★ 84 cited

A semiparametric spatial dynamic model

Yan Sun, Hongjia Yan, Wenyang Zhang +1

Stimulated by the Boston house price data, in this paper, we propose a semiparametric spatial dynamic model, which extends the ordinary spatial autoregressive models to accommodate…

math.ST2014★ 14 cited

Estimating spatial quantile regression with functional coefficients: A robust semiparametric framework

Zudi Lu, Qingguo Tang, Longsheng Cheng

This paper considers an estimation of semiparametric functional (varying)-coefficient quantile regression with spatial data. A general robust framework is developed that treats qua…

math.ST2009★ 109 cited

Specification testing in nonlinear and nonstationary time series autoregression

Jiti Gao, Maxwell King, Zudi Lu +1

This paper considers a class of nonparametric autoregressive models with nonstationarity. We propose a nonparametric kernel test for the conditional mean and then establish an asym…

math.ST2007★ 65 cited

Exploring spatial nonlinearity using additive approximation

Zudi Lu, Arvid Lundervold, Dag Tjøstheim +1

We propose to approximate the conditional expectation of a spatial random variable given its nearest-neighbour observations by an additive function. The setting is meaningful in pr…

math.ST2006★ 108 cited

Estimation in semiparametric spatial regression

Jiti Gao, Zudi Lu, Dag Tjøstheim

Nonparametric methods have been very popular in the last couple of decades in time series and regression, but no such development has taken place for spatial models. A rather obvio…