3 edition of On nonparametric and robust tests for dispersion found in the catalog.
On nonparametric and robust tests for dispersion
Wayne W. Daniel
|Statement||Wayne W. Daniel.|
|Series||Public administration series--bibliography ;, P-382|
|LC Classifications||Z6654.S83 D36, QA277 D36|
|The Physical Object|
|Pagination||10 p. ;|
|Number of Pages||10|
|LC Control Number||80115020|
Tryon, P.V. and Hettmansperger, T.P. (). A class of nonparametric tests for homogeneity against ordered of Statistics, 1, – CrossRef zbMATH MathSciNet Google Scholar. 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.
Skip to main content. University of Jyväskylä. Apply; Study; Research. The newest additions and improvements to probability and statistics functionality focus on data located in space and time. The new spatial analysis functions allow you to find the central location or central data element, depending on the distance function specific to the .
The dispersion is more pronounced for stability i t and law i t. Also, there is some evidence of bi-modality in the year at low levels of governance. These nonparametric regressions methods are robust as they have been tested with a battery of nonparametric misspecification tests. To briefly summarize the results, we find that only. Praise for the Second Edition “This book should be an essential part of the personal library of every practicing statistician.”—Technometrics Thoroughly revised and updated, the new edition of Nonparametric Statistical Methods includes additional modern topics and procedures, more practical data sets, and new problems from real-life situations.
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Get this from a library. On nonparametric and robust tests for dispersion: a selected bibliography. [Wayne W Daniel].
In general, conclusions drawn from non-parametric methods are not as powerful as the parametric ones. However, as non-parametric methods make fewer assumptions, they are more flexible, more robust.
For robust nonparametric hypothesis tests of regression coefficients, the W (Wald) test statistic proposed by DiCiccio and Romano () should be preferred. In this study, the permutation strategy of Huh and Jhun () was found to provide the best control of the type I error, but all permutation methods, except that of Still and White ( Cited by: 1.
In this paper we develop a nonparametric dispersion test for unreplicated two-level fractional factorial designs.
The test statistic is defined, critical values are provided, and large sample. Nonparametric statistics includes nonparametric descriptive statistics, statistical models, inference, and statistical model structure of nonparametric. assumptions do not hold, then nonparametric tests, such as the Wilcoxon–Mann–Whitney and robust rank-order tests, are more appropriate.2 The Wilcoxon–Mann–Whitney test The Wilcoxon–Mann–Whitney test is performed as follows.
The placement of element x i in sample X is deﬁned as the number of lower-valued observations in Y. 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. simulations, nonparametric tests, robust procedures, data transformation, and re-sampling. The word nonparametric is rather associated with rank tests, and ‘robust’ primarily refers to methods for dealing with outliers, but I use the term nonparametric for all situations.
Nonparametric Statistical Methods, Third Edition is an excellent reference for applied statisticians and practitioners who seek a review of nonparametric methods and their relevant applications. The book is also an ideal textbook for upper-undergraduate and first-year graduate courses in applied nonparametric statistics.
A Robust Rank Test for the Behrens–Fisher Problem (Fligner–Policello) Efficiencies of Two-Sample Location Procedures 5. The Two-Sample Dispersion Problem and Other Two-Sample Problems Introduction A Distribution-Free Rank Test for Dispersion–Medians Equal (Ansari–Bradley) Through simulations and examples from the literature, the test is compared to general nonparametric dispersion tests and a parametric test based on a normality assumption.
These comparisons show the test to be the most robust of those studied and even superior to the normality-based test under normality in some situations.
Nonparametric tests of dispersion for the two-period crossover designo. Communications in Statistics - Theory and Methods: Vol. 20, No. 3, pp. Robustness informally refers to how well your classifier performs on examples that have a different distribution than the training set; or alternatively on how minor perturbations to your training set affect accuracy on the test set.
Generally, th. In this work, we generalize the Cramér-von Mises statistic via projection-averaging to obtain a robust test for the multivariate two-sample problem. The proposed test is consistent against all fixed alternatives, robust to heavy-tailed data and minimax rate optimal against a certain class of alternatives.
Our test statistic is completely free of tuning parameters and is computationally. Parametric tests make assumptions about the parameters of a population, whereas nonparametric tests do not include such assumptions or include fewer.
For instance, parametric tests assume that the sample has been randomly selected from the population it represents and that the distribution of data in the population has a known underlying. The book is a collection of some of the research presented at the workshop of the same name held in May at Rutgers University.
The workshop brought together researchers from two different communities: statisticians and specialists in computational geometry.
The main idea unifying these two research areas turned out to be the notion of data depth, which is an important notion both in. Presenting an extensive set of tools and methods for data analysis, Robust Nonparametric Statistical Methods, Second Edition covers univariate tests and estimates with extensions to linear models, multivariate models, times series models, experimental designs, and mixed models.
It follows the approach of the first edition by developing rank-based methods from the unifying theme of geometry. In statistics, dispersion (also called variability, scatter, or spread) is the extent to which a distribution is stretched or squeezed.
Common examples of measures of statistical dispersion are the variance, standard deviation, and interquartile range.
Dispersion is contrasted with location or central tendency, and together they are the most used properties of distributions. A Robust Rank Test for the Behrens–Fisher Problem (Fligner–Policello) Efﬁciencies of Two-Sample Location Procedures 5. The Two-Sample Dispersion Problem and Other Two-Sample Problems Introduction A Distribution-Free Rank Test for Dispersion–Medians Equal (Ansari–Bradley) A Robust Rank Test for the Behrens Fisher Problem (Fligner Policello) Efficiencies of Two-Sample Location Procedures 5.
The Two-Sample Dispersion Problem and Other Two-Sample Problems Introduction A Distribution-Free Rank Test for Dispersion Medians Equal (Ansari Bradley). The coverage is expanded over the first edition to include recent developments in the field. Hettmansperger and McKean examine a wealth of interesting problems in connection with applying nonparametric robust methods.
this is a well-written and nicely presented book that is likely to appeal to a reader with a good mathematical background and an interest in robust and nonparametric Reviews: 1.Combinatorics, hypothesis testing, parametric/non-parametric/robust methods. Chapter 3 (html) The randomization model.
Comparing two treatments in the randomization model. Permutation test, Fisher's exact test. Chapter 4 (html) Ranks, Wilcoxon rank-sum test, tied observations, Siegel-Tukey test, Smirnov test.
Chapter 5 (html).densities, the nonparametric Wilcoxon-matched-pairs-signed-ranks test has proven to be robust and more powerful (Blair & Higgins, ). It is important to note that, although these test statistics were developed for paired data, the tests still assume independent observations within samples.