Two Sample Tests for the Nonparametric Behrens-Fisher Problem with Clustered Data PDF Download
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Author: Denis Larocque
Publisher:
ISBN:
Category :
Languages : en
Pages : 16
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Book Description
Author: Denis Larocque
Publisher:
ISBN:
Category :
Languages : en
Pages : 16
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Book Description
Author: Tae-Hak Park
Publisher:
ISBN:
Category :
Languages : en
Pages : 502
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Book Description
Author: Hannu Oja
Publisher: Springer Science & Business Media
ISBN: 1441904689
Category : Mathematics
Languages : en
Pages : 239
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Book Description
This book offers a new, fairly efficient, and robust alternative to analyzing multivariate data. The analysis of data based on multivariate spatial signs and ranks proceeds very much as does a traditional multivariate analysis relying on the assumption of multivariate normality; the regular L2 norm is just replaced by different L1 norms, observation vectors are replaced by spatial signs and ranks, and so on. A unified methodology starting with the simple one-sample multivariate location problem and proceeding to the general multivariate multiple linear regression case is presented. Companion estimates and tests for scatter matrices are considered as well. The R package MNM is available for computation of the procedures. This monograph provides an up-to-date overview of the theory of multivariate nonparametric methods based on spatial signs and ranks. The classical book by Puri and Sen (1971) uses marginal signs and ranks and different type of L1 norm. The book may serve as a textbook and a general reference for the latest developments in the area. Readers are assumed to have a good knowledge of basic statistical theory as well as matrix theory. Hannu Oja is an academy professor and a professor in biometry in the University of Tampere. He has authored and coauthored numerous research articles in multivariate nonparametrical and robust methods as well as in biostatistics.
Author: Klaus Nordhausen
Publisher: Springer
ISBN: 3319224042
Category : Mathematics
Languages : en
Pages : 513
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Book Description
Written by leading experts in the field, this edited volume brings together the latest findings in the area of nonparametric, robust and multivariate statistical methods. The individual contributions cover a wide variety of topics ranging from univariate nonparametric methods to robust methods for complex data structures. Some examples from statistical signal processing are also given. The volume is dedicated to Hannu Oja on the occasion of his 65th birthday and is intended for researchers as well as PhD students with a good knowledge of statistics.
Author: Akash Roy
Publisher:
ISBN:
Category : Asymptotic distribution (Probability theory)
Languages : en
Pages :
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Book Description
Statistical comparison of two independent groups are one of the most frequently occurring inference problems in scientific research. Most of the existing methods available in the literature are not applicable when measurements are taken with dependent replicates, for example when visual acuity or any blood parameters of mice sharing the same cage are measured. In all these scenarios the replicates should neither be assumed to be independent nor be observations coming from different subjects. Furthermore, using a summary measure of the replicates as a single observation would decrease precision of the effect estimates and thus decrease the powers of the test procedures. Thus, there is a need for purely nonparametric flexible methods that can be used for analyzing such data in a unified way. Ranking procedures are known to be a robust and powerful statistical analysis tool for which parametric distributional assumptions are doubtful. So, a solution is proposed for these two sample problems with correlated replicates. The results achieved in our work generalize the ideas on previous attempts for testing the rather strict hypothesis H0 : F1 = F2 or even for testing H0 : p = 1 2 . In comparison to the existing pioneering works, differently weighted estimators of the treatment effect p as well as unbiased variance estimators will be proposed in the current work. Therefore, it is of major interest to estimate the treatment effect and to test whether there is any significant difference between these two groups along with the computation of a confidence interval. Weighted, unweighted as well as optimal versions of the estimators of the treatment effects are investigated and their asymptotic distributions are derived in a closed form. Furthermore, major attention will be given to the accuracy of the tests in terms of controlling the nominal type-I error level as well as their powers when sample sizes are rather small. Here, it will be shown that the distributions of the tests can be approximated using t-distributions with approximated SatterthwaiteWelch degrees of freedom. The degrees of freedom are estimated in such a way that the new methods coincide with the Brunner-Munzel test when single measurements are observed. Extensive simulation studies show favorable performance of the new methods. Application of this method is extensively shown in four different studies involving small sample sizes and different numbers of dependent replicates per unit.
Author: Ponnapalli Venkata Ramachandramurty
Publisher:
ISBN:
Category :
Languages : en
Pages : 166
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Book Description
Author: John Kloke
Publisher: CRC Press
ISBN: 1439873445
Category : Mathematics
Languages : en
Pages : 283
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Book Description
A Practical Guide to Implementing Nonparametric and Rank-Based Procedures Nonparametric Statistical Methods Using R covers traditional nonparametric methods and rank-based analyses, including estimation and inference for models ranging from simple location models to general linear and nonlinear models for uncorrelated and correlated responses. The authors emphasize applications and statistical computation. They illustrate the methods with many real and simulated data examples using R, including the packages Rfit and npsm. The book first gives an overview of the R language and basic statistical concepts before discussing nonparametrics. It presents rank-based methods for one- and two-sample problems, procedures for regression models, computation for general fixed-effects ANOVA and ANCOVA models, and time-to-event analyses. The last two chapters cover more advanced material, including high breakdown fits for general regression models and rank-based inference for cluster correlated data. The book can be used as a primary text or supplement in a course on applied nonparametric or robust procedures and as a reference for researchers who need to implement nonparametric and rank-based methods in practice. Through numerous examples, it shows readers how to apply these methods using R.
Author:
Publisher:
ISBN:
Category : Weights and measures
Languages : en
Pages : 574
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Book Description
Author: Narayanaswamy Balakrishnan
Publisher: John Wiley & Sons
ISBN: 1118595963
Category : Medical
Languages : en
Pages : 953
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Book Description
Methods and Applications of Statistics in Clinical Trials, Volume 2: Planning, Analysis, and Inferential Methods includes updates of established literature from the Wiley Encyclopedia of Clinical Trials as well as original material based on the latest developments in clinical trials. Prepared by a leading expert, the second volume includes numerous contributions from current prominent experts in the field of medical research. In addition, the volume features: • Multiple new articles exploring emerging topics, such as evaluation methods with threshold, empirical likelihood methods, nonparametric ROC analysis, over- and under-dispersed models, and multi-armed bandit problems • Up-to-date research on the Cox proportional hazard model, frailty models, trial reports, intrarater reliability, conditional power, and the kappa index • Key qualitative issues including cost-effectiveness analysis, publication bias, and regulatory issues, which are crucial to the planning and data management of clinical trials
Author: Vilijandas Bagdonavicius
Publisher: John Wiley & Sons
ISBN: 1118601823
Category : Mathematics
Languages : en
Pages : 191
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Book Description
This book concerns testing hypotheses in non-parametric models. Classical non-parametric tests (goodness-of-fit, homogeneity, randomness, independence) of complete data are considered. Most of the test results are proved and real applications are illustrated using examples. Theories and exercises are provided. The incorrect use of many tests applying most statistical software is highlighted and discussed.
