.png){kind=icon}
返回
Sequential statistical modeling method for distribution type identification
.png){kind=icon}
PRE
AI
DOI：10.1007/s00158-016-1567-2.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
To accurately analyze behavior of mechanical system, accurate statistical modeling of input variables is necessary by identifying probabilistic distributions of input variables. These distributions are generally determined by applying goodness-of-fit (GOF) tests or model selection methods to the given data on the input variables. However, GOF tests only accept or reject the hypothesis that a candidate distribution is appropriate to represent the given data. The model selection methods determine the best-fit distribution for the given data among various candidate distributions but do not provide any information about the adequacy of using the identified distribution to represent the given data. Therefore, in this paper, a sequential statistical modeling (SSM) method is proposed. The SSM method uses a GOF test to select appropriate candidate distributions from among all possible distributions and then identifies the best-fit distribution from among the selected candidate distributions using a model selection method. The adequacy of the identified best-fit distribution is verified by using an area metric that measures the intersection area between the probability density function (PDF) of the best-fit distribution and the data distribution. This metric can be used to analyze the similarities between the PDFs of the candidate distributions. In statistical simulation tests, it was observed that the SSM method identified correct distributions more accurately and conservatively than the GOF tests or model selection methods alone.
Keyword:
Sequential statistical modeling method
Goodness-of-fit test
Model selection method
Area metric
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:
4
论文数:
4.9K
被引数:
1.7W
机构
引用论文
.png){kind=icon}
分享收藏PECVD-grown carbon nanotubes on silicon substrates with a nickel-seeded tip-growth structure具有镍种子尖端生长结构的硅衬底上的PECVD生长的碳纳米管
PREAI
分享收藏
分享收藏Retransmission strategies for cyclic polling over wireless channels in the presence of interference
PREAI
分享收藏Potassium-argon age measurements on dolerites from Antarctica and South Africa来自南极洲和南非的白云岩的钾氩年龄测量
PREAI
分享收藏 没有更多内容
没有更多内容