DISCUSSION

Hallin, Paindaveine and Siman -- hereafter HPS -- are to be congratulated for the appreciation their paper is receiving from The Annals of Statistics. The authors personally are indebted for the attention they devoted to their paper, -- hereafter KM. The topic is quite delicate and multifaceted, hen...

Full description

Saved in:
Bibliographic Details
Published in:The Annals of statistics 2010-04, Vol.38 (2), p.685
Main Authors: Kong, Linglong, Mizera, Ivan
Format: Article
Language:English
Subjects:
Multivariate analysis
Regression analysis
Studies
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page
container_issue 2
container_start_page 685
container_title The Annals of statistics
container_volume 38
creator Kong, Linglong
Mizera, Ivan
description Hallin, Paindaveine and Siman -- hereafter HPS -- are to be congratulated for the appreciation their paper is receiving from The Annals of Statistics. The authors personally are indebted for the attention they devoted to their paper, -- hereafter KM. The topic is quite delicate and multifaceted, hence all the misunderstandings they allege show deficiencies of their exposition rather than anything else. This article discusses certain aspects of using quantiles to obtain insights about multivariate data, suggesting that interesting insights about multivariate data can be obtained by looking at univariate quantiles of projections. Despite all the subtle differences aired here, the authors agree that HPS and KM have a lot in common: they both address depth contours by adopting a sort of directional approach. Regression aspects having been discussed to some extent in the previous section, they concentrate now on the possible impact on the computation of the depth contours (in the static situation, without covariates).
format article
fullrecord <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_851775782</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2267725801</sourcerecordid><originalsourceid>FETCH-proquest_journals_8517757823</originalsourceid><addsrcrecordid>eNpjYuA0MjSz0LWwNDNjYeA0MLA00DU1NjPhYOAqLs4yMDAwtTQx5mTgcvEMdg4NDvb09-NhYE1LzClO5YXS3AxKbq4hzh66BUX5haWpxSXxWfmlRXlAqXgLU0Nzc1NzCyNjohQBAOnEJOM</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>851775782</pqid></control><display><type>article</type><title>DISCUSSION</title><source>JSTOR Archival Journals and Primary Sources Collection</source><source>Project Euclid Complete</source><creator>Kong, Linglong ; Mizera, Ivan</creator><creatorcontrib>Kong, Linglong ; Mizera, Ivan</creatorcontrib><description>Hallin, Paindaveine and Siman -- hereafter HPS -- are to be congratulated for the appreciation their paper is receiving from The Annals of Statistics. The authors personally are indebted for the attention they devoted to their paper, -- hereafter KM. The topic is quite delicate and multifaceted, hence all the misunderstandings they allege show deficiencies of their exposition rather than anything else. This article discusses certain aspects of using quantiles to obtain insights about multivariate data, suggesting that interesting insights about multivariate data can be obtained by looking at univariate quantiles of projections. Despite all the subtle differences aired here, the authors agree that HPS and KM have a lot in common: they both address depth contours by adopting a sort of directional approach. Regression aspects having been discussed to some extent in the previous section, they concentrate now on the possible impact on the computation of the depth contours (in the static situation, without covariates).</description><identifier>ISSN: 0090-5364</identifier><identifier>EISSN: 2168-8966</identifier><language>eng</language><publisher>Hayward: Institute of Mathematical Statistics</publisher><subject>Multivariate analysis ; Regression analysis ; Studies</subject><ispartof>The Annals of statistics, 2010-04, Vol.38 (2), p.685</ispartof><rights>Copyright Institute of Mathematical Statistics Apr 2010</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784</link.rule.ids></links><search><creatorcontrib>Kong, Linglong</creatorcontrib><creatorcontrib>Mizera, Ivan</creatorcontrib><title>DISCUSSION</title><title>The Annals of statistics</title><description>Hallin, Paindaveine and Siman -- hereafter HPS -- are to be congratulated for the appreciation their paper is receiving from The Annals of Statistics. The authors personally are indebted for the attention they devoted to their paper, -- hereafter KM. The topic is quite delicate and multifaceted, hence all the misunderstandings they allege show deficiencies of their exposition rather than anything else. This article discusses certain aspects of using quantiles to obtain insights about multivariate data, suggesting that interesting insights about multivariate data can be obtained by looking at univariate quantiles of projections. Despite all the subtle differences aired here, the authors agree that HPS and KM have a lot in common: they both address depth contours by adopting a sort of directional approach. Regression aspects having been discussed to some extent in the previous section, they concentrate now on the possible impact on the computation of the depth contours (in the static situation, without covariates).</description><subject>Multivariate analysis</subject><subject>Regression analysis</subject><subject>Studies</subject><issn>0090-5364</issn><issn>2168-8966</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><recordid>eNpjYuA0MjSz0LWwNDNjYeA0MLA00DU1NjPhYOAqLs4yMDAwtTQx5mTgcvEMdg4NDvb09-NhYE1LzClO5YXS3AxKbq4hzh66BUX5haWpxSXxWfmlRXlAqXgLU0Nzc1NzCyNjohQBAOnEJOM</recordid><startdate>20100401</startdate><enddate>20100401</enddate><creator>Kong, Linglong</creator><creator>Mizera, Ivan</creator><general>Institute of Mathematical Statistics</general><scope>JQ2</scope></search><sort><creationdate>20100401</creationdate><title>DISCUSSION</title><author>Kong, Linglong ; Mizera, Ivan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-proquest_journals_8517757823</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Multivariate analysis</topic><topic>Regression analysis</topic><topic>Studies</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kong, Linglong</creatorcontrib><creatorcontrib>Mizera, Ivan</creatorcontrib><collection>ProQuest Computer Science Collection</collection><jtitle>The Annals of statistics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kong, Linglong</au><au>Mizera, Ivan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>DISCUSSION</atitle><jtitle>The Annals of statistics</jtitle><date>2010-04-01</date><risdate>2010</risdate><volume>38</volume><issue>2</issue><spage>685</spage><pages>685-</pages><issn>0090-5364</issn><eissn>2168-8966</eissn><abstract>Hallin, Paindaveine and Siman -- hereafter HPS -- are to be congratulated for the appreciation their paper is receiving from The Annals of Statistics. The authors personally are indebted for the attention they devoted to their paper, -- hereafter KM. The topic is quite delicate and multifaceted, hence all the misunderstandings they allege show deficiencies of their exposition rather than anything else. This article discusses certain aspects of using quantiles to obtain insights about multivariate data, suggesting that interesting insights about multivariate data can be obtained by looking at univariate quantiles of projections. Despite all the subtle differences aired here, the authors agree that HPS and KM have a lot in common: they both address depth contours by adopting a sort of directional approach. Regression aspects having been discussed to some extent in the previous section, they concentrate now on the possible impact on the computation of the depth contours (in the static situation, without covariates).</abstract><cop>Hayward</cop><pub>Institute of Mathematical Statistics</pub></addata></record>
fulltext fulltext
identifier ISSN: 0090-5364
ispartof The Annals of statistics, 2010-04, Vol.38 (2), p.685
issn 0090-5364
2168-8966
language eng
recordid cdi_proquest_journals_851775782
source JSTOR Archival Journals and Primary Sources Collection; Project Euclid Complete
subjects Multivariate analysis
Regression analysis
Studies
title DISCUSSION
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-13T00%3A21%3A55IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=DISCUSSION&rft.jtitle=The%20Annals%20of%20statistics&rft.au=Kong,%20Linglong&rft.date=2010-04-01&rft.volume=38&rft.issue=2&rft.spage=685&rft.pages=685-&rft.issn=0090-5364&rft.eissn=2168-8966&rft_id=info:doi/&rft_dat=%3Cproquest%3E2267725801%3C/proquest%3E%3Cgrp_id%3Ecdi_FETCH-proquest_journals_8517757823%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=851775782&rft_id=info:pmid/&rfr_iscdi=true