activity
20052019
most citedLocal linear spatial regression

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

collaborators

9 papers

stat.ME2019

An interval-valued GARCH model for range-measured return processes

Yan Sun, Guanghua Lian, Zudi Lu +2

Range-measured return contains more information than the traditional scalar-valued return. In this paper, we propose to model the [low, high] price range as a random interval and s…

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…

stat.ME2014★ 1 cited

Local Adaptive Grouped Regularization and its Oracle Properties for Varying Coefficient Regression

Wesley Brooks, Jun Zhu, Zudi Lu

Varying coefficient regression is a flexible technique for modeling data where the coefficients are functions of some effect-modifying parameter, often time or location in a certai…

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…