Titlebook: A Parametric Approach to Nonparametric Statistics; Mayer Alvo,Philip L. H. Yu Textbook 2018 Springer Nature Switzerland AG 2018 Nonparamet

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https://doi.org/10.1007/978-1-4419-9158-4introducing the use of penalized likelihood in order to gain further insight into the data. Specifically, if judges provide rankings of . objects, penalized likelihood enables us to focus on those objects which exhibit the greatest differences.

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Antonio Ambrosetti,David Arcoyastatistics whose power function can be determined. The latter is then assessed against the power function of the optimal test statistic derived from Hoeffding’s formula for any given underlying distribution of the data.

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Applications of Fixed Point Theorems,e also consider dispersion alternatives. The tests are derived in the case of the Spearman and the Hamming distance functions. The latter were chosen to exemplify that different approaches may be needed to obtain the asymptotic distributions under the hypotheses.

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2365-5674 ction to probability and statistics.Demonstrates Modern appl.This book demonstrates that nonparametric statistics can be taught from a parametric point of view. As a result, one can exploit various parametric tools such as the use of the likelihood function, penalized likelihood and score functions

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Noncommutative Differential Forms,g with their mean, variance, skewness, and kurtosis. We also describe various modes of convergence and end with central limit theorems. In the section on statistical inference, we begin with the subjects of estimation and hypothesis testing and proceed with the notions of contiguity and composite likelihood.

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