Permutation Methods [electronic resource] : A Distance Function Approach /
The introduction of permutation tests by R. A. Fisher relaxed the paramet ric structure requirement of a test statistic. For example, the structure of the test statistic is no longer required if the assumption of normality is removed. The between-object distance function of classical test statis tics based on the assumption of normality is squared Euclidean distance. Because squared Euclidean distance is not a metric (i. e. , the triangle in equality is not satisfied), it is not at all surprising that classical tests are severely affected by an extreme measurement of a single object. A major purpose of this book is to take advantage of the relaxation of the struc ture of a statistic allowed by permutation tests. While a variety of distance functions are valid for permutation tests, a natural choice possessing many desirable properties is ordinary (i. e. , non-squared) Euclidean distance. Sim ulation studies show that permutation tests based on ordinary Euclidean distance are exceedingly robust in detecting location shifts of heavy-tailed distributions. These tests depend on a metric distance function and are reasonably powerful for a broad spectrum of univariate and multivariate distributions. Least sum of absolute deviations (LAD) regression linked with a per mutation test based on ordinary Euclidean distance yields a linear model analysis which controls for type I error.
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New York, NY : Springer New York : Imprint: Springer,
2001
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Online Access: | http://dx.doi.org/10.1007/978-1-4757-3449-2 |
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KOHA-OAI-TEST:1927902018-07-30T23:17:39ZPermutation Methods [electronic resource] : A Distance Function Approach / Mielke, Paul W. author. Berry, Kenneth J. author. SpringerLink (Online service) textNew York, NY : Springer New York : Imprint: Springer,2001.engThe introduction of permutation tests by R. A. Fisher relaxed the paramet ric structure requirement of a test statistic. For example, the structure of the test statistic is no longer required if the assumption of normality is removed. The between-object distance function of classical test statis tics based on the assumption of normality is squared Euclidean distance. Because squared Euclidean distance is not a metric (i. e. , the triangle in equality is not satisfied), it is not at all surprising that classical tests are severely affected by an extreme measurement of a single object. A major purpose of this book is to take advantage of the relaxation of the struc ture of a statistic allowed by permutation tests. While a variety of distance functions are valid for permutation tests, a natural choice possessing many desirable properties is ordinary (i. e. , non-squared) Euclidean distance. Sim ulation studies show that permutation tests based on ordinary Euclidean distance are exceedingly robust in detecting location shifts of heavy-tailed distributions. These tests depend on a metric distance function and are reasonably powerful for a broad spectrum of univariate and multivariate distributions. Least sum of absolute deviations (LAD) regression linked with a per mutation test based on ordinary Euclidean distance yields a linear model analysis which controls for type I error.1 Introduction -- 2 Description of MRPP -- 3 Further MRPP Applications -- 4 Description of MRBP -- 5 Regression Analysis, Prediction, and Agreement -- 6 Goodness-of-Fit Tests -- 7 Contingency Tables -- 8 Multisample Homogeneity Tests -- A Computer Programs -- A.1 Chapter 2 -- A.2 Chapter 3 -- A.3 Chapter 4 -- A.4 Chapter 5 -- A.5 Chapter 6 -- A.6 Chapter 7 -- A.7 Chapter 8 -- References.The introduction of permutation tests by R. A. Fisher relaxed the paramet ric structure requirement of a test statistic. For example, the structure of the test statistic is no longer required if the assumption of normality is removed. The between-object distance function of classical test statis tics based on the assumption of normality is squared Euclidean distance. Because squared Euclidean distance is not a metric (i. e. , the triangle in equality is not satisfied), it is not at all surprising that classical tests are severely affected by an extreme measurement of a single object. A major purpose of this book is to take advantage of the relaxation of the struc ture of a statistic allowed by permutation tests. While a variety of distance functions are valid for permutation tests, a natural choice possessing many desirable properties is ordinary (i. e. , non-squared) Euclidean distance. Sim ulation studies show that permutation tests based on ordinary Euclidean distance are exceedingly robust in detecting location shifts of heavy-tailed distributions. These tests depend on a metric distance function and are reasonably powerful for a broad spectrum of univariate and multivariate distributions. Least sum of absolute deviations (LAD) regression linked with a per mutation test based on ordinary Euclidean distance yields a linear model analysis which controls for type I error.Statistics.Statistics.Statistical Theory and Methods.Springer eBookshttp://dx.doi.org/10.1007/978-1-4757-3449-2URN:ISBN:9781475734492 |
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Statistics. Statistics. Statistical Theory and Methods. Statistics. Statistics. Statistical Theory and Methods. Mielke, Paul W. author. Berry, Kenneth J. author. SpringerLink (Online service) Permutation Methods [electronic resource] : A Distance Function Approach / |
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The introduction of permutation tests by R. A. Fisher relaxed the paramet ric structure requirement of a test statistic. For example, the structure of the test statistic is no longer required if the assumption of normality is removed. The between-object distance function of classical test statis tics based on the assumption of normality is squared Euclidean distance. Because squared Euclidean distance is not a metric (i. e. , the triangle in equality is not satisfied), it is not at all surprising that classical tests are severely affected by an extreme measurement of a single object. A major purpose of this book is to take advantage of the relaxation of the struc ture of a statistic allowed by permutation tests. While a variety of distance functions are valid for permutation tests, a natural choice possessing many desirable properties is ordinary (i. e. , non-squared) Euclidean distance. Sim ulation studies show that permutation tests based on ordinary Euclidean distance are exceedingly robust in detecting location shifts of heavy-tailed distributions. These tests depend on a metric distance function and are reasonably powerful for a broad spectrum of univariate and multivariate distributions. Least sum of absolute deviations (LAD) regression linked with a per mutation test based on ordinary Euclidean distance yields a linear model analysis which controls for type I error. |
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Texto |
topic_facet |
Statistics. Statistics. Statistical Theory and Methods. |
author |
Mielke, Paul W. author. Berry, Kenneth J. author. SpringerLink (Online service) |
author_facet |
Mielke, Paul W. author. Berry, Kenneth J. author. SpringerLink (Online service) |
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Mielke, Paul W. author. |
title |
Permutation Methods [electronic resource] : A Distance Function Approach / |
title_short |
Permutation Methods [electronic resource] : A Distance Function Approach / |
title_full |
Permutation Methods [electronic resource] : A Distance Function Approach / |
title_fullStr |
Permutation Methods [electronic resource] : A Distance Function Approach / |
title_full_unstemmed |
Permutation Methods [electronic resource] : A Distance Function Approach / |
title_sort |
permutation methods [electronic resource] : a distance function approach / |
publisher |
New York, NY : Springer New York : Imprint: Springer, |
publishDate |
2001 |
url |
http://dx.doi.org/10.1007/978-1-4757-3449-2 |
work_keys_str_mv |
AT mielkepaulwauthor permutationmethodselectronicresourceadistancefunctionapproach AT berrykennethjauthor permutationmethodselectronicresourceadistancefunctionapproach AT springerlinkonlineservice permutationmethodselectronicresourceadistancefunctionapproach |
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