How to estimate statistical characteristics based on a sample: Nonparametric maximum likelihood approach leads to sample mean, sample variance, etc.
© Springer International Publishing AG 2018. In many practical situations, we need to estimate different statistical characteristics based on a sample. In some cases, we know that the corresponding probability distribution belongs to a known finite-parametric family of distributions. In such cases,...
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th-cmuir.6653943832-439122018-01-24T04:15:03Z How to estimate statistical characteristics based on a sample: Nonparametric maximum likelihood approach leads to sample mean, sample variance, etc. Vladik Kreinovich Thongchai Dumrongpokaphan © Springer International Publishing AG 2018. In many practical situations, we need to estimate different statistical characteristics based on a sample. In some cases, we know that the corresponding probability distribution belongs to a known finite-parametric family of distributions. In such cases, a reasonable idea is to use the Maximum Likelihood method to estimate the corresponding parameters, and then to compute the value of the desired statistical characteristic for the distribution with these parameters. In some practical situations, we do not know any family containing the unknown distribution. We show that in such nonparametric cases, the Maximum Likelihood approach leads to the use of sample mean, sample variance, etc. 2018-01-24T04:15:03Z 2018-01-24T04:15:03Z 2018-01-01 Book Series 1860949X 2-s2.0-85037861764 10.1007/978-3-319-70942-0_11 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85037861764&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/43912 |
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© Springer International Publishing AG 2018. In many practical situations, we need to estimate different statistical characteristics based on a sample. In some cases, we know that the corresponding probability distribution belongs to a known finite-parametric family of distributions. In such cases, a reasonable idea is to use the Maximum Likelihood method to estimate the corresponding parameters, and then to compute the value of the desired statistical characteristic for the distribution with these parameters. In some practical situations, we do not know any family containing the unknown distribution. We show that in such nonparametric cases, the Maximum Likelihood approach leads to the use of sample mean, sample variance, etc. |
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Book Series |
| author |
Vladik Kreinovich Thongchai Dumrongpokaphan |
| spellingShingle |
Vladik Kreinovich Thongchai Dumrongpokaphan How to estimate statistical characteristics based on a sample: Nonparametric maximum likelihood approach leads to sample mean, sample variance, etc. |
| author_facet |
Vladik Kreinovich Thongchai Dumrongpokaphan |
| author_sort |
Vladik Kreinovich |
| title |
How to estimate statistical characteristics based on a sample: Nonparametric maximum likelihood approach leads to sample mean, sample variance, etc. |
| title_short |
How to estimate statistical characteristics based on a sample: Nonparametric maximum likelihood approach leads to sample mean, sample variance, etc. |
| title_full |
How to estimate statistical characteristics based on a sample: Nonparametric maximum likelihood approach leads to sample mean, sample variance, etc. |
| title_fullStr |
How to estimate statistical characteristics based on a sample: Nonparametric maximum likelihood approach leads to sample mean, sample variance, etc. |
| title_full_unstemmed |
How to estimate statistical characteristics based on a sample: Nonparametric maximum likelihood approach leads to sample mean, sample variance, etc. |
| title_sort |
how to estimate statistical characteristics based on a sample: nonparametric maximum likelihood approach leads to sample mean, sample variance, etc. |
| publishDate |
2018 |
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https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85037861764&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/43912 |
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