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Constructing a multivariate distribution function with a vine copula: towards multivariate luminosity and mass functions
http://hdl.handle.net/2237/0002001360
http://hdl.handle.net/2237/0002001360ffc709c6-f878-486e-9916-2c0b504edb17
名前 / ファイル | ライセンス | アクション |
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Item type | itemtype_ver1(1) | |||||||||
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公開日 | 2021-09-07 | |||||||||
タイトル | ||||||||||
タイトル | Constructing a multivariate distribution function with a vine copula: towards multivariate luminosity and mass functions | |||||||||
言語 | en | |||||||||
著者 |
Takeuchi, Tsutomu T
× Takeuchi, Tsutomu T
× Kono, Kai T
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アクセス権 | ||||||||||
アクセス権 | open access | |||||||||
アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||||||
権利 | ||||||||||
言語 | en | |||||||||
権利情報 | [Monthly Notices of the Royal Astronomical Society, Volume 498, Issue 3, November 2020, Pages 4365–4378], [Constructing a multivariate distribution function with a vine copula: towards multivariate luminosity and mass functions] by [Tsutomu T Takeuchi, Kai T Kono], [2020], reproduced by permission of Oxford University Press [https://doi.org/10.1093/mnras/staa2558] | |||||||||
キーワード | ||||||||||
主題Scheme | Other | |||||||||
主題 | ISM: atoms | |||||||||
キーワード | ||||||||||
主題Scheme | Other | |||||||||
主題 | ISM: molecules | |||||||||
キーワード | ||||||||||
主題Scheme | Other | |||||||||
主題 | galaxies: evolution | |||||||||
キーワード | ||||||||||
主題Scheme | Other | |||||||||
主題 | galaxies: formation | |||||||||
キーワード | ||||||||||
主題Scheme | Other | |||||||||
主題 | galaxies: luminosity function | |||||||||
キーワード | ||||||||||
主題Scheme | Other | |||||||||
主題 | mass function | |||||||||
キーワード | ||||||||||
主題Scheme | Other | |||||||||
主題 | galaxies: star formation | |||||||||
内容記述 | ||||||||||
内容記述 | The need for a method to construct multidimensional distribution function is increasing recently, in the era of huge multiwavelength surveys. We have proposed a systematic method to build a bivariate luminosity or mass function of galaxies by using a copula. It allows us to construct a distribution function when only its marginal distributions are known, and we have to estimate the dependence structure from data. A typical example is the situation that we have univariate luminosity functions at some wavelengths for a survey, but the joint distribution is unknown. Main limitation of the copula method is that it is not easy to extend a joint function to higher dimensions (d > 2), except some special cases like multidimensional Gaussian. Even if we find such a multivariate analytic function in some fortunate case, it would often be inflexible and impractical. In this work, we show a systematic method to extend the copula method to unlimitedly higher dimensions by a vine copula. This is based on the pair-copula decomposition of a general multivariate distribution. We show how the vine copula construction is flexible and extendable. We also present an example of the construction of a stellar mass–atomic gas–molecular gas three-dimensional mass function. We demonstrate the maximum likelihood estimation of the best functional form for this function, as well as a proper model selection via vine copula. | |||||||||
言語 | en | |||||||||
内容記述タイプ | Abstract | |||||||||
出版者 | ||||||||||
言語 | en | |||||||||
出版者 | Oxford University Press | |||||||||
言語 | ||||||||||
言語 | eng | |||||||||
資源タイプ | ||||||||||
資源タイプresource | http://purl.org/coar/resource_type/c_6501 | |||||||||
タイプ | journal article | |||||||||
出版タイプ | ||||||||||
出版タイプ | VoR | |||||||||
出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||
関連情報 | ||||||||||
関連タイプ | isVersionOf | |||||||||
識別子タイプ | DOI | |||||||||
関連識別子 | https://doi.org/10.1093/mnras/staa2558 | |||||||||
収録物識別子 | ||||||||||
収録物識別子タイプ | PISSN | |||||||||
収録物識別子 | 0035-8711 | |||||||||
書誌情報 |
en : Monthly Notices of the Royal Astronomical Society 巻 498, 号 3, p. 4365-4378, 発行日 2020-11 |
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ファイル公開日 | ||||||||||
日付 | 2021-09-07 | |||||||||
日付タイプ | Available |