Classifiability and Designs for Sampling

For the problem of classifying observations into one of several distributions conditions are obtained for controlling misclassification errors arbitrarily and uniformly in terms of the structure of the underlying distributions. In order to control errors it is pointed out that it is not necessary in...

Full description

Saved in:
Bibliographic Details
Published in:Sankhya. Series A 1974-07, Vol.36 (3), p.237-250
Main Authors: Gupta, Somesh Das, Kinderman, Albert
Format: Article
Language:English
Subjects:
Distance functions
Error rates
Euclidean space
Gaussian distributions
Grants
Mathematical moments
Mathematical theorems
Product distribution
Random variables
Sufficient conditions
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page 250
container_issue 3
container_start_page 237
container_title Sankhya. Series A
container_volume 36
creator Gupta, Somesh Das
Kinderman, Albert
description For the problem of classifying observations into one of several distributions conditions are obtained for controlling misclassification errors arbitrarily and uniformly in terms of the structure of the underlying distributions. In order to control errors it is pointed out that it is not necessary in some cases to draw samples from all the unknown distributions. Classification into multivariate normal distributions is discussed to illustrate the theories.
format article
fullrecord <record><control><sourceid>jstor</sourceid><recordid>TN_cdi_jstor_primary_25049936</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><jstor_id>25049936</jstor_id><sourcerecordid>25049936</sourcerecordid><originalsourceid>FETCH-jstor_primary_250499363</originalsourceid><addsrcrecordid>eNpjYeA0MLUw1DU1N4rgYOAqLs4yMDA1N7Qw4WTQcM5JLC7OTMtMTMrMySypVEjMS1FwSS3OTM8rVkjLL1IITswtyMnMS-dhYE1LzClO5YXS3Ayybq4hzh66WcUl-UXxBUWZuYlFlfFGpgYmlpbGZsaE5AEfOitq</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Classifiability and Designs for Sampling</title><source>JSTOR</source><creator>Gupta, Somesh Das ; Kinderman, Albert</creator><creatorcontrib>Gupta, Somesh Das ; Kinderman, Albert</creatorcontrib><description>For the problem of classifying observations into one of several distributions conditions are obtained for controlling misclassification errors arbitrarily and uniformly in terms of the structure of the underlying distributions. In order to control errors it is pointed out that it is not necessary in some cases to draw samples from all the unknown distributions. Classification into multivariate normal distributions is discussed to illustrate the theories.</description><identifier>ISSN: 0581-572X</identifier><language>eng</language><publisher>Statistical Publishing Society</publisher><subject>Distance functions ; Error rates ; Euclidean space ; Gaussian distributions ; Grants ; Mathematical moments ; Mathematical theorems ; Product distribution ; Random variables ; Sufficient conditions</subject><ispartof>Sankhya. Series A, 1974-07, Vol.36 (3), p.237-250</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/25049936$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/25049936$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>314,776,780,58217,58450</link.rule.ids></links><search><creatorcontrib>Gupta, Somesh Das</creatorcontrib><creatorcontrib>Kinderman, Albert</creatorcontrib><title>Classifiability and Designs for Sampling</title><title>Sankhya. Series A</title><description>For the problem of classifying observations into one of several distributions conditions are obtained for controlling misclassification errors arbitrarily and uniformly in terms of the structure of the underlying distributions. In order to control errors it is pointed out that it is not necessary in some cases to draw samples from all the unknown distributions. Classification into multivariate normal distributions is discussed to illustrate the theories.</description><subject>Distance functions</subject><subject>Error rates</subject><subject>Euclidean space</subject><subject>Gaussian distributions</subject><subject>Grants</subject><subject>Mathematical moments</subject><subject>Mathematical theorems</subject><subject>Product distribution</subject><subject>Random variables</subject><subject>Sufficient conditions</subject><issn>0581-572X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1974</creationdate><recordtype>article</recordtype><sourceid/><recordid>eNpjYeA0MLUw1DU1N4rgYOAqLs4yMDA1N7Qw4WTQcM5JLC7OTMtMTMrMySypVEjMS1FwSS3OTM8rVkjLL1IITswtyMnMS-dhYE1LzClO5YXS3Ayybq4hzh66WcUl-UXxBUWZuYlFlfFGpgYmlpbGZsaE5AEfOitq</recordid><startdate>19740701</startdate><enddate>19740701</enddate><creator>Gupta, Somesh Das</creator><creator>Kinderman, Albert</creator><general>Statistical Publishing Society</general><scope/></search><sort><creationdate>19740701</creationdate><title>Classifiability and Designs for Sampling</title><author>Gupta, Somesh Das ; Kinderman, Albert</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-jstor_primary_250499363</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1974</creationdate><topic>Distance functions</topic><topic>Error rates</topic><topic>Euclidean space</topic><topic>Gaussian distributions</topic><topic>Grants</topic><topic>Mathematical moments</topic><topic>Mathematical theorems</topic><topic>Product distribution</topic><topic>Random variables</topic><topic>Sufficient conditions</topic><toplevel>online_resources</toplevel><creatorcontrib>Gupta, Somesh Das</creatorcontrib><creatorcontrib>Kinderman, Albert</creatorcontrib><jtitle>Sankhya. Series A</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Gupta, Somesh Das</au><au>Kinderman, Albert</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Classifiability and Designs for Sampling</atitle><jtitle>Sankhya. Series A</jtitle><date>1974-07-01</date><risdate>1974</risdate><volume>36</volume><issue>3</issue><spage>237</spage><epage>250</epage><pages>237-250</pages><issn>0581-572X</issn><abstract>For the problem of classifying observations into one of several distributions conditions are obtained for controlling misclassification errors arbitrarily and uniformly in terms of the structure of the underlying distributions. In order to control errors it is pointed out that it is not necessary in some cases to draw samples from all the unknown distributions. Classification into multivariate normal distributions is discussed to illustrate the theories.</abstract><pub>Statistical Publishing Society</pub></addata></record>
fulltext fulltext
identifier ISSN: 0581-572X
ispartof Sankhya. Series A, 1974-07, Vol.36 (3), p.237-250
issn 0581-572X
language eng
recordid cdi_jstor_primary_25049936
source JSTOR
subjects Distance functions
Error rates
Euclidean space
Gaussian distributions
Grants
Mathematical moments
Mathematical theorems
Product distribution
Random variables
Sufficient conditions
title Classifiability and Designs for Sampling
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-21T10%3A13%3A19IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-jstor&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Classifiability%20and%20Designs%20for%20Sampling&rft.jtitle=Sankhya.%20Series%20A&rft.au=Gupta,%20Somesh%20Das&rft.date=1974-07-01&rft.volume=36&rft.issue=3&rft.spage=237&rft.epage=250&rft.pages=237-250&rft.issn=0581-572X&rft_id=info:doi/&rft_dat=%3Cjstor%3E25049936%3C/jstor%3E%3Cgrp_id%3Ecdi_FETCH-jstor_primary_250499363%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_jstor_id=25049936&rfr_iscdi=true