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:
Our new developments are accompanied by respective algorithms and R codes, available at GitHub, and as such readily available for applications.
M-statistics is suitable for professionals and students alike. It is highly useful for theoretical statisticians and teachers, researchers, and data science analysts as an alternative to classical and approximate statistical inference.
"Sinopsis" puede pertenecer a otra edición de este libro.
Eugene Demidenko is Professor of Biomedical Data Science at the Geisel School of Medicine and Mathematics at Dartmouth. He is a member of the American Statistical Association (ASA) and the Society of Industrial and Applied Mathematics (SIAM). In statistics, Professor Demidenko’s research includes statistical methodology, mixed models, and nonlinear regression. In applied mathematics, he contributed to existence and uniqueness of global minimum, tumor regrowth theory, shape and image analysis, and solving ill-posed problems via mixed boundary partial differential equations. He is the author of two books published by Wiley in 2013 and 2020 “Mixed Models: Theory and Applications” and “Advanced Statistics with Applications in R.” The latter book received a prestigious Ziegel Book Award in Statistics from Technometrics/ASA journal in 2022.
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:
Our new developments are accompanied by respective algorithms and R codes, available at GitHub, and as such readily available for applications.
M-statistics is suitable for professionals and students alike. It is highly useful for theoretical statisticians and teachers, researchers, and data science analysts as an alternative to classical and approximate statistical inference.
"Sobre este título" puede pertenecer a otra edición de este libro.
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