高斯空间过程的复合似然估计效率研究

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY Accounts of Chemical Research Pub Date : 2024-01-01 DOI:10.5705/ss.202020.0311
N. Chua, Francis K. C. Hui, A. Welsh
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引用次数: 0

摘要

最大复合似然估计是标准最大似然估计的一种有吸引力且常用的替代方法,标准最大似然估计通常涉及牺牲统计效率以提高计算效率。这种统计效率可以通过评估最大复合似然估计量的三明治信息矩阵来量化,然后将其与最大似然估计量的类似Fisher信息矩阵进行比较。本文导出了一维指数协方差高斯过程的各种极大复合似然估计的渐近相对效率的新的封闭表达式。这些表达式基于一种抽样方案,该方案允许在三种常见的空间渐近框架下进行分析:扩展域、填充和混合。我们的结果证明了复合似然的选择如何影响估计的效率和一致性,特别是对于填充和混合框架。
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On the Efficiency of Composite Likelihood Estimation for Gaussian Spatial Processes
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来源期刊
Accounts of Chemical Research
Accounts of Chemical Research 化学-化学综合
CiteScore
31.40
自引率
1.10%
发文量
312
审稿时长
2 months
期刊介绍: Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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