Local signal detection on irregular domains with generalized varying coefficient models

IF 3 1区数学 Q1 STATISTICS & PROBABILITY

Journal of the American Statistical Association Pub Date : 2024-11-05 DOI:10.1080/01621459.2024.2423972

Chengzhu Zhang, Lan Xue, Yu Chen, Heng Lian, Annie Qu

引用次数: 0

Abstract

In spatial analysis, it is essential to understand and quantify spatial or temporal heterogeneity. This paper focuses on the generalized spatially varying coefficient model (GSVCM), a powerful fram...

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利用广义变化系数模型在不规则域上进行局部信号检测

在空间分析中，理解和量化空间或时间异质性至关重要。本文的重点是广义空间变化系数模型（GSVCM），这是一个功能强大的框架。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of the American Statistical Association 数学-统计学与概率论

CiteScore

7.50

自引率

8.10%

发文量

168

审稿时长

12 months

期刊介绍： Established in 1888 and published quarterly in March, June, September, and December, the Journal of the American Statistical Association ( JASA ) has long been considered the premier journal of statistical science. Articles focus on statistical applications, theory, and methods in economic, social, physical, engineering, and health sciences. Important books contributing to statistical advancement are reviewed in JASA . JASA is indexed in Current Index to Statistics and MathSci Online and reviewed in Mathematical Reviews. JASA is abstracted by Access Company and is indexed and abstracted in the SRM Database of Social Research Methodology.