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M-statistics : Optimal Statistical Inference for a Small Sample by Eugene Demidenko is a nonfiction available to read on EtoBox.

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M-STATISTICS A comprehensive resource providing new statistical methodologies and demonstrating how new approaches work for applications M-statistics introduces a new approach to statistical inference, redesigning the fundamentals of statistics, and improving on the classical methods we already use. This book targets exact optimal statistical inference for a small sample under one methodological umbrella. Two competing approaches are offered: maximum concentration (MC) and mode (MO) statistics combined under one methodological umbrella, which is why the symbolic equation M=MC+MO. M-statistics defines an estimator as the limit point of the MC or MO exact optimal confidence interval when the confidence level approaches zero, the MC and MO estimator, respectively. Neither mean nor variance plays a role in M-statistics theory. Novel statistical methodologies in the form of double-sided unbiased and short confidence intervals and tests apply to major statistical parameters: Exact statistical inference for small sample sizes is illustrated with effect size and coefficient of variation, the rate parameter of the Pareto distribution, two-sample statistical inference for normal variance

Who reads M-statistics : Optimal Statistical Inference for a Small Sample?

Author

Eugene Demidenko

Publisher

Wiley & Sons, Limited, John; Wiley

Published

2023

Language

EN

ISBN

9781119891819

Category

nonfiction

Subjects

Computer Science, Mathematics, Stem