词条 | Neutral vector |
释义 |
In statistics, and specifically in the study of the Dirichlet distribution, a neutral vector of random variables is one that exhibits a particular type of statistical independence amongst its elements.[1] In particular, when elements of the random vector must add up to certain sum, then an element in the vector is neutral with respect to the others if the distribution of the vector created by expressing the remaining elements as proportions of their total is independent of the element that was omitted. DefinitionA single element of a random vector is neutral if the relative proportions of all the other elements are independent of . Formally, consider the vector of random variables where The values are interpreted as lengths whose sum is unity. In a variety of contexts, it is often desirable to eliminate a proportion, say , and consider the distribution of the remaining intervals within the remaining length. The first element of , viz is defined as neutral if is statistically independent of the vector Variable is neutral if is independent of the remaining interval: that is, being independent of Thus , viewed as the first element of , is neutral. In general, variable is neutral if is independent of Complete neutralityA vector for which each element is neutral is completely neutral. If is drawn from a Dirichlet distribution, then is completely neutral. In 1980, James and Mosimann[2] showed that the Dirichlet distribution is characterised by neutrality. See also
References1. ^{{Cite journal | last1 = Connor | first1 = R. J. | last2 = Mosimann | first2 = J. E. | title = Concepts of Independence for Proportions with a Generalization of the Dirichlet Distribution | journal = Journal of the American Statistical Association | volume = 64 | issue = 325 | pages = 194–206 | doi = 10.2307/2283728 | year = 1969 | pmid = | pmc = }} 2. ^{{cite journal| last=James| first=Ian R.| author2 =Mosimann, James E|title=A new characterization of the Dirichlet distribution through neutrality| journal=The Annals of Statistics| year=1980| volume=8| number=1| pages=183–189| postscript=.| doi=10.1214/aos/1176344900}} 2 : Theory of probability distributions|Independence (probability theory) |
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