Kunio Shimizu – författare

Presenting the first comprehensive review of the subject's theory and applications inmore than 15 years, this outstanding reference encompasses the most-up-to-date advances in lognormal distributions in thorough, detailed contributions by specialists in statistics, business and economics, industry, biology, ecology, geology, and meteorology. Lognormal Distributions describes the theory and methods of point and intervalestimation as well as the testing of hypotheses clearly and precisely from a modemviewpoint-not only for the basic two-parameter lognormal distribution but also for itsgeneralizations, including three parameters, truncated distributions, delta-lognormaldistributions, and two or more dimensions.Featuring over 600 references plus author and subject indexes, this volume rev iews thesubject's history .. . gives explicit formulas for minimum variance unbiased estimates ofparameters and their variances ... provides optimal tests of hypotheses and confidenceinterval procedures for various functions of the parameters in the two-parameter model. .. and discusses practical methods of analysis for truncated, censored, or groupedsamples.

Presenting the first comprehensive review of the subject's theory and applications inmore than 15 years, this outstanding reference encompasses the most-up-to-date advances in lognormal distributions in thorough, detailed contributions by specialists in statistics, business and economics, industry, biology, ecology, geology, and meteorology. Lognormal Distributions describes the theory and methods of point and intervalestimation as well as the testing of hypotheses clearly and precisely from a modemviewpoint-not only for the basic two-parameter lognormal distribution but also for itsgeneralizations, including three parameters, truncated distributions, delta-lognormaldistributions, and two or more dimensions.Featuring over 600 references plus author and subject indexes, this volume rev iews thesubject's history .. . gives explicit formulas for minimum variance unbiased estimates ofparameters and their variances ... provides optimal tests of hypotheses and confidenceinterval procedures for various functions of the parameters in the two-parameter model. .. and discusses practical methods of analysis for truncated, censored, or groupedsamples.

A text that focuses on methods of deriving probability distributions on smooth manifolds popular in applied research Probability Distributions for Directional Data on Smooth Manifolds is a comprehensive text that reviews real-life scientific problems that encounter non-linear data. The authors noted experts on the topic present methods for developing distributions on a circle and show how such methods are generalized for other manifolds. In addition, the book explores new methods unique to other manifolds such as disc and hyperdisc. Designed to be an accessible resource, the book explores the methods from the beginning for a simple manifold and clearly demonstrates how these unfold and generalize to more complicated manifolds. Rather than treating one distribution at a time, the authors develop the generalizations of the methods of derivations. As the outcomes of these generalizations are reviewed, new distributions are presented. In addition, the book provides several illustrative, real-life examples, which not only attest to the ongoing usefulness of these important distributions but can help visualize other modern-day areas of the applications. This important resource: Offers a compilation of the probability distributions of the various manifolds presented Covers many emerging distributions and topics which have not been covered in any book to date Presents discussions on axial distributions, distributions on the sphere, hemi-sphere and hyper-sphere, and distributions on the (ring) torus and hyper-torus Contains information on distributions on the (vertical) cylinder and hyper-cylinder, distributions on the disc and hyper-disc, isotropy and goodness of fit tests, and mathematical formulae Written for students of mathematics and statistics and theoretical researchers, Probability Distributions for Directional Data on Smooth Manifolds, the first of its kind, offers a groundbreaking and authoritative guide to the topic.

A text that focuses on methods of deriving probability distributions on smooth manifolds popular in applied research Probability Distributions for Directional Data on Smooth Manifolds is a comprehensive text that reviews real-life scientific problems that encounter non-linear data. The authors—noted experts on the topic—present methods for developing distributions on a circle and show how such methods are generalized for other manifolds. In addition, the book explores new methods unique to other manifolds such as disc and hyperdisc. Designed to be an accessible resource, the book explores the methods from the beginning for a simple manifold and clearly demonstrates how these unfold and generalize to more complicated manifolds. Rather than treating one distribution at a time, the authors develop the generalizations of the methods of derivations. As the outcomes of these generalizations are reviewed, new distributions are presented. In addition, the book provides several illustrative, real-life examples, which not only attest to the ongoing usefulness of these important distributions but can help visualize other modern-day areas of the applications. This important resource: Offers a compilation of the probability distributions of the various manifolds presentedCovers many emerging distributions and topics which have not been covered in any book to datePresents discussions on axial distributions, distributions on the sphere, hemi-sphere and hyper-sphere, and distributions on the (ring) torus and hyper-torusContains information on distributions on the (vertical) cylinder and hyper-cylinder, distributions on the disc and hyper-disc, isotropy and goodness of fit tests, and mathematical formulaeWritten for students of mathematics and statistics and theoretical researchers, Probability Distributions for Directional Data on Smooth Manifolds, the first of its kind, offers a groundbreaking and authoritative guide to the topic.

Presenting the first comprehensive review of the subject''s theory and applications inmore than 15 years, this outstanding reference encompasses the most-up-to-date advancesin lognormal distributions in thorough, detailed contributions by specialists in statistics,business and economics , industry, biology , ecology, geology, and meteorology.Lognormal Distributions describes the theory and methods of point and intervalestimation as well as the testing of hypotheses clearly and precisely from a modemviewpoint-not only for the basic two-parameter lognormal distribution but also for itsgeneralizations, including three parameters, truncated distributions, delta-lognormaldistributions, and two or more dimensions.Featuring over 600 references plus author and subject indexes, this volume rev iews thesubject''s history .. . gives explicit formulas for minimum variance unbiased estimates ofparameters and their variances ... provides optimal tests of hypotheses and confidenceinterval procedures for various functions of the parameters in the two-parameter model. .. and discusses practical methods of analysis for truncated, censored, or groupedsamples.

Presenting the first comprehensive review of the subject''s theory and applications inmore than 15 years, this outstanding reference encompasses the most-up-to-date advancesin lognormal distributions in thorough, detailed contributions by specialists in statistics,business and economics , industry, biology , ecology, geology, and meteorology.Lognormal Distributions describes the theory and methods of point and intervalestimation as well as the testing of hypotheses clearly and precisely from a modemviewpoint-not only for the basic two-parameter lognormal distribution but also for itsgeneralizations, including three parameters, truncated distributions, delta-lognormaldistributions, and two or more dimensions.Featuring over 600 references plus author and subject indexes, this volume rev iews thesubject''s history .. . gives explicit formulas for minimum variance unbiased estimates ofparameters and their variances ... provides optimal tests of hypotheses and confidenceinterval procedures for various functions of the parameters in the two-parameter model. .. and discusses practical methods of analysis for truncated, censored, or groupedsamples.

This book contains select chapters on a range of topics in directional statistics, multivariate statistical inference, financial statistics, statistical machine learning and reliability inference. At the 43rd Annual Convention of the Indian Society for Probability and Statistics (ISPS) held in Prayagraj (formerly Allahabad), Uttar Pradesh, India, from 6–8 February 2024, attribute was paid to Prof. Ashis SenGupta on the occasion of his 70th birthday. He has pioneered research on directional statistics in the modern era in India and enhanced it worldwide and contributed significantly to the advancement of the following topics:Highly flexible distributions on manifoldsStatistical machine learning in data scienceBig data on manifoldsOptimal multiparameter, multivariate statistical inferenceReliability inference and stress-dependent-strength modelsDirectional statistics for highly volatile financial modelsCylindrical, spherical and toroidal regression analysisInnovative applications of emerging real-life directional data

This book contains select chapters on a range of topics in directional statistics, multivariate statistical inference, financial statistics, statistical machine learning and reliability inference. At the 43rd Annual Convention of the Indian Society for Probability and Statistics (ISPS) held in Prayagraj (formerly Allahabad), Uttar Pradesh, India, from 6–8 February 2024, attribute was paid to Prof. Ashis SenGupta on the occasion of his 70th birthday. He has pioneered research on directional statistics in the modern era in India and enhanced it worldwide and contributed significantly to the advancement of the following topics:

Highly flexible distributions on manifoldsStatistical machine learning in data scienceBig data on manifoldsOptimal multiparameter, multivariate statistical inferenceReliability inference and stress-dependent-strength modelsDirectional statistics for highly volatile financial modelsCylindrical, spherical and toroidal regression analysisInnovative applications of emerging real-life directional data