Robust Likelihoods
作者:
Tsung-Shan Tsou,
RichardM. Royall,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1995)
卷期:
Volume 90,
issue 429
页码: 316-320
ISSN:0162-1459
年代: 1995
DOI:10.1080/01621459.1995.10476515
出版商: Taylor & Francis Group
关键词: Evidential analysis;Fisher information;Fixed points comparison;Likelihood function;Robustness;Support intervals
数据来源: Taylor
摘要:
Strong theoretical arguments imply that for directly representing and interpreting statistical data as evidence, the proper vehicle is the likelihood function. These arguments have had limited impact on statistical practice. One reason for this is that likelihood functions are explicitly model dependent and thus appear to be inherently nonrobust. In this article we examine the concept of robustness as it relates to likelihood functions. We note five ways that likelihood functions can be used to represent and interpret statistical data as evidence. These various uses suggest corresponding senses in which one likelihood function can approximate another, and these in turn suggest different senses in which a likelihood function can be “robust.” We establish some general relationships among these senses of robustness, and examine two general techniques for producing robust likelihoods.
点击下载:
PDF (610KB)
返 回