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On the Product of a Singular Wishart Matrix and a Singular Gaussian Vector in High DimensionPrimeFaces.cw("AccordionPanel","widget_formSmash_some",{id:"formSmash:some",widgetVar:"widget_formSmash_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_all",{id:"formSmash:all",widgetVar:"widget_formSmash_all",multiple:true});
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2018 (English)In: Theory of Probability and Mathematical Statistics, ISSN 0094-9000, Vol. 99, p. 37-50Article in journal (Refereed) Published
##### Abstract [en]

##### Place, publisher, year, edition, pages

2018. Vol. 99, p. 37-50
##### Keywords [en]

Singular Wishart distribution, singular normal distribution, stochastic representation, high-dimensional asymptotics
##### National Category

Probability Theory and Statistics
##### Research subject

Mathematical Statistics
##### Identifiers

URN: urn:nbn:se:su:diva-164884ISI: 000493467200004OAI: oai:DiVA.org:su-164884DiVA, id: diva2:1280632
#####

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PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt1129",{id:"formSmash:j_idt1129",widgetVar:"widget_formSmash_j_idt1129",multiple:true});
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PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt1135",{id:"formSmash:j_idt1135",widgetVar:"widget_formSmash_j_idt1135",multiple:true}); Available from: 2019-01-20 Created: 2019-01-20 Last updated: 2020-04-27Bibliographically approved
##### In thesis

In this paper we consider the product of a singular Wishart random matrix and a singular normal random vector. A very useful stochastic representation of this product is derived, using which its characteristic function and asymptotic distribution under the double asymptotic regime are established. We further document a good finite sample performance of the obtained high-dimensional asymptotic distribution via an extensive Monte Carlo study.

1. Statistical Inference of Tangency Portfolio in Small and Large Dimension$(function(){PrimeFaces.cw("OverlayPanel","overlay1426116",{id:"formSmash:j_idt1409:0:j_idt1413",widgetVar:"overlay1426116",target:"formSmash:j_idt1409:0:parentLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

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