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...
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Veröffentlicht in: | Biometrics 1974-12, Vol.30 (4), p.573-581 |
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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 |
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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. 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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> |
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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 |
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