Multiple Comparisons Using R, Bretz, Frank

Автор: Gomes, Maria Isabel,
Название: Mathematical models for decision making with multiple perspectives :
ISBN: 0367440741 ISBN-13(EAN): 9780367440749
Издательство: Taylor&Francis
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Цена: 153120.00 T
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Описание: This book brings together, in a single volume, the fields of multicriteria decision making and multiobjective optimization that are traditionally covered by different books. It is written in a didactic form using examples to help understanding of the proposed methodologies better.

Автор: He, Yulei
Название: Multiple Imputation Analysis For Ob
ISBN: 1498722067 ISBN-13(EAN): 9781498722063
Издательство: Taylor&Francis
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Цена: 91860.00 T
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Описание: Multiple Imputation of Missing Data in Practice: Basic Theory and Analysis Strategies provides a comprehensive introduction to the multiple imputation approach to missing data problems that are often encountered in data analysis.

Автор: Taka-aki Shiraishi; Hiroshi Sugiura; Shin-ichi Mat
Название: Pairwise Multiple Comparisons
ISBN: 9811500657 ISBN-13(EAN): 9789811500657
Издательство: Springer
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Цена: 55890.00 T
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Описание: This book focuses on all-pairwise multiple comparisons of means in multi-sample models, introducing closed testing procedures based on maximum absolute values of some two-sample t-test statistics and on F-test statistics in homoscedastic multi-sample models. It shows that (1) the multi-step procedures are more powerful than single-step procedures and the Ryan/Einot–Gabriel/Welsh tests, and (2) the confidence regions induced by the multi-step procedures are equivalent to simultaneous confidence intervals. Next, it describes the multi-step test procedure in heteroscedastic multi-sample models, which is superior to the single-step Games–Howell procedure. In the context of simple ordered restrictions of means, the authors also discuss closed testing procedures based on maximum values of two-sample one-sided t-test statistics and based on Bartholomew's statistics. Furthermore, the book presents distribution-free procedures and describes simulation studies performed under the null hypothesis and some alternative hypotheses. Although single-step multiple comparison procedures are generally used, the closed testing procedures described are more powerful than the single-step procedures. In order to execute the multiple comparison procedures, the upper 100? percentiles of the complicated distributions are required. Classical integral formulas such as Simpson's rule and the Gaussian rule have been used for the calculation of the integral transform that appears in statistical calculations. However, these formulas are not effective for the complicated distribution. As such, the authors introduce the sinc method, which is optimal in terms of accuracy and computational cost.

Автор: Shiraishi
Название: Multiple Comparisons for Bernoulli Data
ISBN: 9811927073 ISBN-13(EAN): 9789811927072
Издательство: Springer
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Цена: 51230.00 T
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Описание: This book focuses on multiple comparisons of proportions in multi-sample models with Bernoulli responses. First, the author explains the one-sample and two-sample methods that form the basis of multiple comparisons. Then, regularity conditions are stated in detail. Simultaneous inference for all proportions based on exact confidence limits and based on asymptotic theory is discussed. Closed testing procedures based on some one-sample statistics are introduced. For all-pairwise multiple comparisons of proportions, the author uses arcsine square root transformation of sample means. Closed testing procedures based on maximum absolute values of some two-sample test statistics and based on chi-square test statistics are introduced. It is shown that the multi-step procedures are more powerful than single-step procedures and the Ryan–Einot–Gabriel–Welsch (REGW)-type tests. Furthermore, the author discusses multiple comparisons with a control. Under simple ordered restrictions of proportions, the author also discusses closed testing procedures based on maximum values of two-sample test statistics and based on Bartholomew's statistics. Last, serial gatekeeping procedures based on the above-mentioned closed testing procedures are proposed although Bonferroni inequalities are used in serial gatekeeping procedures of many.

Название: Handbook of Multiple Comparisons
ISBN: 0367140675 ISBN-13(EAN): 9780367140670
Издательство: Taylor&Francis
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Цена: 219470.00 T
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Описание: Treats the topics of multiple comparisons, simultaneous and selective inference from avariety of different perspectives. The need for a systematic treatment of the eld originates from the relevanceof multiple comparisons in many applications (medicine, industry, economics), and from the diversityof approaches and developments.

Автор: Hoppe, Fred. M.
Название: Multiple Comparisons, Selection and Applications in Biometry
ISBN: 0824788958 ISBN-13(EAN): 9780824788957
Издательство: Taylor&Francis
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Цена: 275610.00 T
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Автор: Cui, Xinping ; Dickhaus, Thorsten ; Ding, Ying
Название: Handbook of Multiple Comparisons
ISBN: 1032111550 ISBN-13(EAN): 9781032111551
Издательство: Taylor&Francis
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Цена: 83690.00 T
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Автор: Hsu, Jason
Название: Multiple Comparisons
ISBN: 0412982811 ISBN-13(EAN): 9780412982811
Издательство: Taylor&Francis
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Цена: 163330.00 T
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Автор: Hsu, Jason
Название: Multiple Comparisons
ISBN: 1032478020 ISBN-13(EAN): 9781032478029
Издательство: Taylor&Francis
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Цена: 46950.00 T
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Автор: Berglund Patricia, Heeringa Steven G.
Название: Multiple Imputation of Missing Data Using SAS
ISBN: 1612904521 ISBN-13(EAN): 9781612904528
Издательство: Неизвестно
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Цена: 56340.00 T
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Название: Multiple Imputation in Practice
ISBN: 1498770169 ISBN-13(EAN): 9781498770163
Издательство: Taylor&Francis
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Цена: 78590.00 T
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Автор: Pages
Название: Multiple Factor Analysis by Example Using R
ISBN: 1482205475 ISBN-13(EAN): 9781482205473
Издательство: Taylor&Francis
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Цена: 86760.00 T
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Описание: Multiple factor analysis (MFA) enables users to analyze tables of individuals and variables in which the variables are structured into quantitative, qualitative, or mixed groups. Written by the co-developer of this methodology, Multiple Factor Analysis by Example Using R brings together the theoretical and methodological aspects of MFA. It also includes examples of applications and details of how to implement MFA using an R package (FactoMineR). The first two chapters cover the basic factorial analysis methods of principal component analysis (PCA) and multiple correspondence analysis (MCA). The next chapter discusses factor analysis for mixed data (FAMD), a little-known method for simultaneously analyzing quantitative and qualitative variables without group distinction. Focusing on MFA, subsequent chapters examine the key points of MFA in the context of quantitative variables as well as qualitative and mixed data. The author also compares MFA and Procrustes analysis and presents a natural extension of MFA: hierarchical MFA (HMFA). The final chapter explores several elements of matrix calculation and metric spaces used in the book.