.png){kind=icon}
返回
Implementing likelihood-based inference for fat-tailed distributions
.png){kind=icon}
OA
AI
DOI：10.1016/j.frl.2007.12.004.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The theoretical advancements in higher-order likelihood-based inference methods have been tremendous over the past two decades. The application of these methods in the applied literature however has been far from widespread. A critical barrier to adoption has likely been the computational difficulties associated with the implementation of these methods. This paper provides the applied researcher with a systematic exposition of the calculations and computer code required to implement the higher-order conditional inference methodology of Fraser and Reid [1995. Utilitas Mathematica 47, 33-53] for problems involving heavy- or fat-tailed distributions. (c) 2007 Elsevier Inc. All rights reserved.
Keyword:
Third-order inference
p-Values
Likelihood
Fat-tailed distributions
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.9
论文数:
9.2K
被引数:
2.8W
机构
引用论文
.png){kind=icon}
Regression analysis, nonlinear or nonnormal:: Simple and accurate p values from likelihood analysis
PREAI
分享收藏分享收藏
没有更多内容
没有更多内容