QMPE:用参数相关的下界估计对数正态、瓦尔德和威布尔RT分布。

Andrew Heathcote, Scott Brown, Denis Cousineau
{"title":"QMPE:用参数相关的下界估计对数正态、瓦尔德和威布尔RT分布。","authors":"Andrew Heathcote,&nbsp;Scott Brown,&nbsp;Denis Cousineau","doi":"10.3758/bf03195574","DOIUrl":null,"url":null,"abstract":"<p><p>We describe and test quantile maximum probability estimator (QMPE), an open-source ANSI Fortran 90 program for response time distribution estimation. QMPE enables users to estimate parameters for the ex-Gaussian and Gumbel (1958) distributions, along with three \"shifted\" distributions (i.e., distributions with a parameter-dependent lower bound): the Lognormal, Wald, and Weibul distributions. Estimation can be performed using either the standard continuous maximum likelihood (CML) method or quantile maximum probability (QMP; Heathcote & Brown, in press). We review the properties of each distribution and the theoretical evidence showing that CML estimates fail for some cases with shifted distributions, whereas QMP estimates do not. In cases in which CML does not fail, a Monte Carlo investigation showed that QMP estimates were usually as good, and in some cases better, than CML estimates. However, the Monte Carlo study also uncovered problems that can occur with both CML and QMP estimates, particularly when samples are small and skew is low, highlighting the difficulties of estimating distributions with parameter-dependent lower bounds.</p>","PeriodicalId":79800,"journal":{"name":"Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc","volume":"36 2","pages":"277-90"},"PeriodicalIF":0.0000,"publicationDate":"2004-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3758/bf03195574","citationCount":"153","resultStr":"{\"title\":\"QMPE: estimating Lognormal, Wald, and Weibull RT distributions with a parameter-dependent lower bound.\",\"authors\":\"Andrew Heathcote,&nbsp;Scott Brown,&nbsp;Denis Cousineau\",\"doi\":\"10.3758/bf03195574\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>We describe and test quantile maximum probability estimator (QMPE), an open-source ANSI Fortran 90 program for response time distribution estimation. QMPE enables users to estimate parameters for the ex-Gaussian and Gumbel (1958) distributions, along with three \\\"shifted\\\" distributions (i.e., distributions with a parameter-dependent lower bound): the Lognormal, Wald, and Weibul distributions. Estimation can be performed using either the standard continuous maximum likelihood (CML) method or quantile maximum probability (QMP; Heathcote & Brown, in press). We review the properties of each distribution and the theoretical evidence showing that CML estimates fail for some cases with shifted distributions, whereas QMP estimates do not. In cases in which CML does not fail, a Monte Carlo investigation showed that QMP estimates were usually as good, and in some cases better, than CML estimates. However, the Monte Carlo study also uncovered problems that can occur with both CML and QMP estimates, particularly when samples are small and skew is low, highlighting the difficulties of estimating distributions with parameter-dependent lower bounds.</p>\",\"PeriodicalId\":79800,\"journal\":{\"name\":\"Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc\",\"volume\":\"36 2\",\"pages\":\"277-90\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2004-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.3758/bf03195574\",\"citationCount\":\"153\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3758/bf03195574\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3758/bf03195574","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 153

摘要

本文描述并测试了一个用于响应时间分布估计的开源ANSI Fortran 90程序——分位数最大概率估计器(QMPE)。QMPE使用户能够估计前高斯分布和Gumbel(1958)分布的参数,以及三个“移位”分布(即具有参数依赖下界的分布):对数正态分布,Wald分布和Weibul分布。估计可以使用标准连续最大似然(CML)方法或分位数最大概率(QMP;希斯考特与布朗出版社出版)。我们回顾了每个分布的性质和理论证据,表明CML估计在某些情况下会失败,而QMP估计则不会。在CML没有失败的情况下,蒙特卡罗调查表明,QMP估计通常与CML估计一样好,在某些情况下甚至更好。然而,蒙特卡罗研究也揭示了CML和QMP估计可能出现的问题,特别是当样本小且偏度低时,突出了估计具有参数依赖下界的分布的困难。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
QMPE: estimating Lognormal, Wald, and Weibull RT distributions with a parameter-dependent lower bound.

We describe and test quantile maximum probability estimator (QMPE), an open-source ANSI Fortran 90 program for response time distribution estimation. QMPE enables users to estimate parameters for the ex-Gaussian and Gumbel (1958) distributions, along with three "shifted" distributions (i.e., distributions with a parameter-dependent lower bound): the Lognormal, Wald, and Weibul distributions. Estimation can be performed using either the standard continuous maximum likelihood (CML) method or quantile maximum probability (QMP; Heathcote & Brown, in press). We review the properties of each distribution and the theoretical evidence showing that CML estimates fail for some cases with shifted distributions, whereas QMP estimates do not. In cases in which CML does not fail, a Monte Carlo investigation showed that QMP estimates were usually as good, and in some cases better, than CML estimates. However, the Monte Carlo study also uncovered problems that can occur with both CML and QMP estimates, particularly when samples are small and skew is low, highlighting the difficulties of estimating distributions with parameter-dependent lower bounds.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信