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  1. B100 理学部/理学研究科
  2. B100a 雑誌掲載論文
  3. 学術雑誌

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/2237/0002001360
ffc709c6-f878-486e-9916-2c0b504edb17
名前 / ファイル ライセンス アクション
staa2558.pdf staa2558.pdf (2.4 MB)
Item type itemtype_ver1(1)
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

en Takeuchi, Tsutomu T

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Kono, Kai T

× Kono, Kai T

en 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
ファイル公開日
日付 2021-09-07
日付タイプ Available
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