M-Statistics : Optimal Statistical Inference for a Small Sample

M-Statistics Optimal Statistical Inference for a Small Sample

"M-statistics: A New Statistical Perspective introduces a new approach for statistical interference, redesigning the fundamentals of statistics and improving on the classical methods we already use. The author discusses the development of new criteria for efficient estimation and delves into ho...

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Detalles Bibliográficos
Autor principal: Demidenko, Eugene (-)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Newark : John Wiley & Sons, Incorporated 2023.
Edición:1st ed
Materias:
Mathematical statistics.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009769038106719
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright
  • Contents
  • Preface
  • Chapter 1 Limitations of classic statistics and motivation
  • 1.1 Limitations of classic statistics
  • 1.1.1 Mean
  • 1.1.2 Unbiasedness
  • 1.1.3 Limitations of equal‐tail statistical inference
  • 1.2 The rationale for a new statistical theory
  • 1.3 Motivating example: normal variance
  • 1.3.1 Confidence interval for the normal variance
  • 1.3.2 Hypothesis testing for the variance
  • 1.3.3 MC and MO estimators of the variance
  • 1.3.4 Sample size determination for variance
  • 1.4 Neyman‐Pearson lemma and its extensions
  • 1.4.1 Introduction
  • 1.4.2 Two lemmas
  • References
  • Chapter 2 Maximum concentration statistics
  • 2.1 Assumptions
  • 2.2 Short confidence interval and MC estimator
  • 2.3 Density level test
  • 2.4 Efficiency and the sufficient statistic
  • 2.5 Parameter is positive or belongs to a finite interval
  • 2.5.1 Parameter is positive
  • 2.5.2 Parameter belongs to a finite interval
  • References
  • Chapter 3 Mode statistics
  • 3.1 Unbiased test
  • 3.2 Unbiased CI and MO estimator
  • 3.3 Cumulative information and the sufficient statistic
  • References
  • Chapter 4 P‐value and duality
  • 4.1 P‐value for the double‐sided hypothesis
  • 4.1.1 General definition
  • 4.1.2 P‐value for normal variance
  • 4.2 The overall powerful test
  • 4.3 Duality: converting the CI into a hypothesis test
  • 4.4 Bypassing assumptions
  • 4.5 Overview
  • References
  • Chapter 5 M‐statistics for major statistical parameters
  • 5.1 Exact statistical inference for standard deviation
  • 5.1.1 MC‐statistics
  • 5.1.2 MC‐statistics on the log scale
  • 5.1.3 MO‐statistics
  • 5.1.4 Computation of the p‐value
  • 5.2 Pareto distribution
  • 5.2.1 Confidence intervals
  • 5.2.2 Hypothesis testing
  • 5.3 Coefficient of variation for lognormal distribution
  • 5.4 Statistical testing for two variances.
  • 5.4.1 Computation of the p‐value
  • 5.4.2 Optimal sample size
  • 5.5 Inference for two‐sample exponential distribution
  • 5.5.1 Unbiased statistical test
  • 5.5.2 Confidence intervals
  • 5.5.3 The MC estimator of ν
  • 5.6 Effect size and coefficient of variation
  • 5.6.1 Effect size
  • 5.6.2 Coefficient of variation
  • 5.6.3 Double‐sided hypothesis tests
  • 5.6.4 Multivariate ES
  • 5.7 Binomial probability
  • 5.7.1 The MCL estimator
  • 5.7.2 The MCL2 estimator
  • 5.7.3 The MCL2 estimator of pn
  • 5.7.4 Confidence interval on the double‐log scale
  • 5.7.5 Equal‐tail and unbiased tests
  • 5.8 Poisson rate
  • 5.8.1 Two‐sided short CI on the log scale
  • 5.8.2 Two‐sided tests and p‐value
  • 5.8.3 The MCL estimator of the rate parameter
  • 5.9 Meta‐analysis model
  • 5.9.1 CI and MCL estimator
  • 5.10 M‐statistics for the correlation coefficient
  • 5.10.1 MC and MO estimators
  • 5.10.2 Equal‐tail and unbiased tests
  • 5.10.3 Power function and p‐value
  • 5.10.4 Confidence intervals
  • 5.11 The square multiple correlation coefficient
  • 5.11.1 Unbiased statistical test
  • 5.11.2 Computation of p‐value
  • 5.11.3 Confidence intervals
  • 5.11.4 The two‐sided CI on the log scale
  • 5.11.5 The MCL estimator
  • 5.12 Coefficient of determination for linear model
  • 5.12.1 CoD and multiple correlation coefficient
  • 5.12.2 Unbiased test
  • 5.12.3 The MCL estimator for CoD
  • References
  • Chapter 6 Multidimensional parameter
  • 6.1 Density level test
  • 6.2 Unbiased test
  • 6.3 Confidence region dual to the DL test
  • 6.4 Unbiased confidence region
  • 6.5 Simultaneous inference for normal mean and standard deviation
  • 6.5.1 Statistical test
  • 6.5.2 Confidence region
  • 6.6 Exact confidence inference for parameters of the beta distribution
  • 6.6.1 Statistical tests
  • 6.6.2 Confidence regions
  • 6.7 Two‐sample binomial probability
  • 6.7.1 Hypothesis testing.
  • 6.7.2 Confidence region
  • 6.8 Exact and profile statistical inference for nonlinear regression
  • 6.8.1 Statistical inference for the whole parameter
  • 6.8.2 Statistical inference for an individual parameter of interest via profiling
  • References
  • Index
  • EULA.

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