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Please use this identifier to cite or link to this item: http://hdl.handle.net/10928/452

Title: 多変量線形モデルにおける高次元漸近理論
Other Titles: High-dimensional asymptotic theory for multivariate linear model
Authors: 姫野, 哲人
HIMENO, Tetsuto
Keywords: high-dimension
asymptotic theory
multivariate linear model
Issue Date: 1-Dec-2013
Publisher: 成蹊大学理工学部
Abstract: When a statistic with a complicated distribution is dealt, the asymptotic distribution is often used. Even if the exact distribution is complicated, the asymptotic distribution generally becomes simple form such as normal distribution and chi square distribution. There are also previous studies which derive the asymptotic correction due to improve the approximation. However it is empirically known that the classical asymptotic approximations become worse as the dimension becomes large. So we derive some high-dimensional asymptotic results for the multivariate linear model. These results have better approximations in spite of the size of dimension. These results not only derive better approximation but also clarify asymptotic properties of some test statistics.
URI: http://hdl.handle.net/10928/452
Appears in Collections:第50巻第2号

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