Exact and Approximate Tests for Unbalanced Random Effects Designs

In the three stage nested design with random effects, let $\sigma_A^2$ be the variance of factor A (the top stage). It is often desirable to test the hypothesis H$_A$ : $\sigma_A^2$ = 0. In the unbalanced case, the conventional F-test for H$_A$ does not in general have the expected null distribution...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Biometrics 1974-12, Vol.30 (4), p.573-581
1. Verfasser: Tietjen, Gary L.
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 581
container_issue 4
container_start_page 573
container_title Biometrics
container_volume 30
creator Tietjen, Gary L.
description In the three stage nested design with random effects, let $\sigma_A^2$ be the variance of factor A (the top stage). It is often desirable to test the hypothesis H$_A$ : $\sigma_A^2$ = 0. In the unbalanced case, the conventional F-test for H$_A$ does not in general have the expected null distribution, and the expected mean squares are not in general equal under H$_A$. Tietjen and Moore [1968] proposed that a Satterthwaite-like procedure be used to construct a denominator (and D.F.) which would have, under H$_A$, the same expected mean square as the numerator. It was pointed out by W. H. Kruskal, however, that such a procedure had no justification since the mean squares were not independent and did not have chi-square distributions. This paper is an attempt to revisit the question by investigating the properties of this approximation and those of the conventional F-test. It is shown that the conventional F-test does considerably better as an approximation under imbalance than does the Satterthwaite test.
doi_str_mv 10.2307/2529222
format Article
fullrecord <record><control><sourceid>jstor_cross</sourceid><recordid>TN_cdi_crossref_primary_10_2307_2529222</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><jstor_id>2529222</jstor_id><sourcerecordid>2529222</sourcerecordid><originalsourceid>FETCH-LOGICAL-c252t-5bf78bc785e8f744cef352081656377518ba2e339636c5a35c583d7f6c405313</originalsourceid><addsrcrecordid>eNp1j01LxDAYhIMoWFfxL-QgeKq--XiT9FjW9QMWBKngraRpIrvstiXpYf33RnavnoZhHoYZQm4ZPHAB-pEjrzjnZ6RgKFkJksM5KQBAlUKyr0tyldI22wqBF6ReHaybqR16Wk9THA-bvZ09bXyaEw1jpJ9DZ3d2cL6nH5ka93QVgnc5ffJp8z2ka3IR7C75m5MuSPO8apav5fr95W1Zr0uXF80ldkGbzmmD3gQtpfNBIAfDFCqhNTLTWe6FqJRQDq1Ah0b0OignAQUTC3J_rHVxTCn60E4xb40_LYP273h7Op7JuyO5TfMY_8V-AXgWU9g</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Exact and Approximate Tests for Unbalanced Random Effects Designs</title><source>JSTOR Mathematics &amp; Statistics</source><source>JSTOR Archive Collection A-Z Listing</source><creator>Tietjen, Gary L.</creator><creatorcontrib>Tietjen, Gary L.</creatorcontrib><description>In the three stage nested design with random effects, let $\sigma_A^2$ be the variance of factor A (the top stage). It is often desirable to test the hypothesis H$_A$ : $\sigma_A^2$ = 0. In the unbalanced case, the conventional F-test for H$_A$ does not in general have the expected null distribution, and the expected mean squares are not in general equal under H$_A$. Tietjen and Moore [1968] proposed that a Satterthwaite-like procedure be used to construct a denominator (and D.F.) which would have, under H$_A$, the same expected mean square as the numerator. It was pointed out by W. H. Kruskal, however, that such a procedure had no justification since the mean squares were not independent and did not have chi-square distributions. This paper is an attempt to revisit the question by investigating the properties of this approximation and those of the conventional F-test. It is shown that the conventional F-test does considerably better as an approximation under imbalance than does the Satterthwaite test.</description><identifier>ISSN: 0006-341X</identifier><identifier>EISSN: 1541-0420</identifier><identifier>DOI: 10.2307/2529222</identifier><language>eng</language><publisher>Biometric Society</publisher><subject>Analysis of variance ; Approximation ; Biometrics ; Degrees of freedom ; Experiment design ; Hypothesis testing ; Null hypothesis ; P values ; Statistical variance ; Uniformity</subject><ispartof>Biometrics, 1974-12, Vol.30 (4), p.573-581</ispartof><rights>Copyright 1974 The Biometric Society</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c252t-5bf78bc785e8f744cef352081656377518ba2e339636c5a35c583d7f6c405313</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/2529222$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/2529222$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>315,781,785,804,833,27928,27929,58021,58025,58254,58258</link.rule.ids></links><search><creatorcontrib>Tietjen, Gary L.</creatorcontrib><title>Exact and Approximate Tests for Unbalanced Random Effects Designs</title><title>Biometrics</title><description>In the three stage nested design with random effects, let $\sigma_A^2$ be the variance of factor A (the top stage). It is often desirable to test the hypothesis H$_A$ : $\sigma_A^2$ = 0. In the unbalanced case, the conventional F-test for H$_A$ does not in general have the expected null distribution, and the expected mean squares are not in general equal under H$_A$. Tietjen and Moore [1968] proposed that a Satterthwaite-like procedure be used to construct a denominator (and D.F.) which would have, under H$_A$, the same expected mean square as the numerator. It was pointed out by W. H. Kruskal, however, that such a procedure had no justification since the mean squares were not independent and did not have chi-square distributions. This paper is an attempt to revisit the question by investigating the properties of this approximation and those of the conventional F-test. It is shown that the conventional F-test does considerably better as an approximation under imbalance than does the Satterthwaite test.</description><subject>Analysis of variance</subject><subject>Approximation</subject><subject>Biometrics</subject><subject>Degrees of freedom</subject><subject>Experiment design</subject><subject>Hypothesis testing</subject><subject>Null hypothesis</subject><subject>P values</subject><subject>Statistical variance</subject><subject>Uniformity</subject><issn>0006-341X</issn><issn>1541-0420</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1974</creationdate><recordtype>article</recordtype><recordid>eNp1j01LxDAYhIMoWFfxL-QgeKq--XiT9FjW9QMWBKngraRpIrvstiXpYf33RnavnoZhHoYZQm4ZPHAB-pEjrzjnZ6RgKFkJksM5KQBAlUKyr0tyldI22wqBF6ReHaybqR16Wk9THA-bvZ09bXyaEw1jpJ9DZ3d2cL6nH5ka93QVgnc5ffJp8z2ka3IR7C75m5MuSPO8apav5fr95W1Zr0uXF80ldkGbzmmD3gQtpfNBIAfDFCqhNTLTWe6FqJRQDq1Ah0b0OignAQUTC3J_rHVxTCn60E4xb40_LYP273h7Op7JuyO5TfMY_8V-AXgWU9g</recordid><startdate>19741201</startdate><enddate>19741201</enddate><creator>Tietjen, Gary L.</creator><general>Biometric Society</general><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>19741201</creationdate><title>Exact and Approximate Tests for Unbalanced Random Effects Designs</title><author>Tietjen, Gary L.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c252t-5bf78bc785e8f744cef352081656377518ba2e339636c5a35c583d7f6c405313</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1974</creationdate><topic>Analysis of variance</topic><topic>Approximation</topic><topic>Biometrics</topic><topic>Degrees of freedom</topic><topic>Experiment design</topic><topic>Hypothesis testing</topic><topic>Null hypothesis</topic><topic>P values</topic><topic>Statistical variance</topic><topic>Uniformity</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Tietjen, Gary L.</creatorcontrib><collection>CrossRef</collection><jtitle>Biometrics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Tietjen, Gary L.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Exact and Approximate Tests for Unbalanced Random Effects Designs</atitle><jtitle>Biometrics</jtitle><date>1974-12-01</date><risdate>1974</risdate><volume>30</volume><issue>4</issue><spage>573</spage><epage>581</epage><pages>573-581</pages><issn>0006-341X</issn><eissn>1541-0420</eissn><abstract>In the three stage nested design with random effects, let $\sigma_A^2$ be the variance of factor A (the top stage). It is often desirable to test the hypothesis H$_A$ : $\sigma_A^2$ = 0. In the unbalanced case, the conventional F-test for H$_A$ does not in general have the expected null distribution, and the expected mean squares are not in general equal under H$_A$. Tietjen and Moore [1968] proposed that a Satterthwaite-like procedure be used to construct a denominator (and D.F.) which would have, under H$_A$, the same expected mean square as the numerator. It was pointed out by W. H. Kruskal, however, that such a procedure had no justification since the mean squares were not independent and did not have chi-square distributions. This paper is an attempt to revisit the question by investigating the properties of this approximation and those of the conventional F-test. It is shown that the conventional F-test does considerably better as an approximation under imbalance than does the Satterthwaite test.</abstract><pub>Biometric Society</pub><doi>10.2307/2529222</doi><tpages>9</tpages></addata></record>
fulltext fulltext
identifier ISSN: 0006-341X
ispartof Biometrics, 1974-12, Vol.30 (4), p.573-581
issn 0006-341X
1541-0420
language eng
recordid cdi_crossref_primary_10_2307_2529222
source JSTOR Mathematics & Statistics; JSTOR Archive Collection A-Z Listing
subjects Analysis of variance
Approximation
Biometrics
Degrees of freedom
Experiment design
Hypothesis testing
Null hypothesis
P values
Statistical variance
Uniformity
title Exact and Approximate Tests for Unbalanced Random Effects Designs
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-16T22%3A58%3A10IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-jstor_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Exact%20and%20Approximate%20Tests%20for%20Unbalanced%20Random%20Effects%20Designs&rft.jtitle=Biometrics&rft.au=Tietjen,%20Gary%20L.&rft.date=1974-12-01&rft.volume=30&rft.issue=4&rft.spage=573&rft.epage=581&rft.pages=573-581&rft.issn=0006-341X&rft.eissn=1541-0420&rft_id=info:doi/10.2307/2529222&rft_dat=%3Cjstor_cross%3E2529222%3C/jstor_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_jstor_id=2529222&rfr_iscdi=true