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[期刊论文]

Statistical inference for semiparametric varying -coefficient spatial autoregressive models under restricted conditions

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Author:

Luo, Guowang (Luo, Guowang.) | Wu, Mixia (Wu, Mixia.) (Scholars:吴密霞)

Indexed by:

EI Scopus SCIE

Abstract:

This article considers statistical inference for restricted semiparametric varying-coefficient spatial autoregressive(SVCSAR) models. We propose a restricted estimation method for parametric and nonparametric components, and a Lagrange-multiplier-type test for testing hypotheses on the parametric component restrictions of SVCSAR models. Under mild conditions, we obtain the asymptotic normality for the resulting estimator of the parametric vector and the optimal convergence rate for that of nonparametric functions. Simulation studies are carried out to investigate the finite sample performance of the proposed method. The method is exemplified with Boston housing price data.

Keyword:

Restricted conditions Semiparametric spatial autoregression Instrumental variable B-spline basis function

Author Community:

  • [ 1 ] [Luo, Guowang]Beijing Univ Technol, Coll Appl Sci, Beijing 100124, Peoples R China
  • [ 2 ] [Wu, Mixia]Beijing Univ Technol, Coll Appl Sci, Beijing 100124, Peoples R China

Reprint Author's Address:

  • 吴密霞

    [Wu, Mixia]Beijing Univ Technol, Coll Appl Sci, Beijing 100124, Peoples R China

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Source :

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION

ISSN: 0361-0918

Year: 2019

Issue: 5

Volume: 51

Page: 2268-2286

0 . 9 0 0

JCR@2022

ESI Discipline: MATHEMATICS;

ESI HC Threshold:54

Cited Count:

WoS CC Cited Count: 4

SCOPUS Cited Count: 3

30 Days PV: 2

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