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Constrained Bayesian Methods of Hypotheses Testing: A New Philosophy of Hypotheses Testing in Parallel and Sequential Experiments

Autor Kartlos Kachiashvili
en Limba Engleză Hardback – apr 2018

Considerăm că elementul central al volumului Constrained Bayesian Methods of Hypotheses Testing îl reprezintă capitolul al șaptelea, dedicat investigațiilor experimentale riguroase. Aici, Kartlos Kachiashvili supune noile sale metode (CBM) unor teste comparative cu algoritmi consacrați, oferind cititorului date concrete despre eficiența acestui nou cadru teoretic. Această componentă practică este susținută de prezentarea unui software dedicat în capitolul șase, instrument esențial pentru cercetătorii care doresc să aplice direct aceste soluții în analiza datelor.

Structura cărții reflectă o progresie logică, de la contextul istoric al metodelor Fisher, Neyman-Pearson sau Wald, până la formularea problemelor Bayesiene cu restricții. Merită menționat că autorul dedică spații ample analizei secvențiale și distribuțiilor normale, oferind o perspectivă tehnică dar aplicată. Lucrarea completează viziunea din Sequential Analysis de Alexander Tartakovsky, adăugând cadrul specific al metodelor Bayesiene cu restricții acolo unde Tartakovsky se concentrează pe detecția punctelor de schimbare și pe probleme de decizie multiplă. Totodată, față de abordarea filosofică din The Use of Restricted Significance Tests in Clinical Trials de David S. Salsburg, volumul de față oferă o fundamentare matematică mai vastă, aplicabilă dincolo de domeniul studiilor clinice.

În contextul operei sale anterioare, precum Numerical Methods and Computational Algorithms in Applied Mathematics, această carte reprezintă o rafinare a interesului autorului pentru algoritmizare și aplicabilitatea matematicii. Dacă în lucrările precedente accentul cădea pe algoritmi de calcul generali, aici Kartlos Kachiashvili își concentrează întreaga expertiză acumulată din anii '70 pe o nișă critică a statisticii matematice, transformând o viață de cercetare într-o metodologie unitară.

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Specificații

ISBN-13: 9781536131031
ISBN-10: 1536131032
Pagini: 224
Dimensiuni: 178 x 254 x 26 mm
Greutate: 0.79 kg
Editura: Nova Science Publishers Inc
Colecția Nova Science Publishers, Inc (US)
Locul publicării:United States

De ce să citești această carte

Recomandăm această lucrare studenților de la masterat și doctorat, precum și cercetătorilor care utilizează statistica aplicată. Cititorul câștigă acces la o metodologie inovatoare (CBM) și la instrumentele software necesare implementării acesteia. Este o resursă valoroasă pentru cei care doresc să depășească limitele testării clasice a ipotezelor, beneficiind de un model care integrează restricții matematice în experimente paralele și secvențiale.


Descriere

The problems of one of the basic branches of mathematical statistics statistical hypotheses testing are considered in this book. The intensive development of these methods began at the beginning of the last century. The basic results of modern theory of statistical hypotheses testing belong to the cohort of famous statisticians of this period: Fisher, Neyman-Pearson, Jeffreys and Wald (Fisher, 1925; Neyman and Pearson, 1928, 1933; Jeffreys, 1939; Wald, 1947a,b). Many other bright scientists have brought their invaluable contributions to the development of this theory and practice. As a result of their efforts, many brilliant methods for different suppositions about the character of random phenomena are under study, as well as their applications for solving very complicated and diverse modern problems. Since the mid-1970s, the author of this book has been engaged in the development of the methods of statistical hypotheses testing and their applications for solving practical problems from different spheres of human activity. As a result of this activity, a new approach to the solution of the considered problem has been developed, which was later named the Constrained Bayesian Methods (CBM) of statistical hypotheses testing. Decades were dedicated to the description, investigation and applications of these methods for solving different problems. The results obtained for the current century are collected in seven chapters and three appendices of this book. The short descriptions of existing basic methods of statistical hypotheses testing in relation to different CBM are examined in Chapter One. The formulations and solutions of conventional (unconstrained) and new (constrained) Bayesian problems of hypotheses testing are described in Chapter Two. The investigation of singularities of hypotheses acceptance regions in CBM and new opportunities in hypotheses testing are presented in Chapter Three. Chapter Four is devoted to the investigations for normal distribution. Sequential analysis approaches developed on the basis of CBM for different kinds of hypotheses are described in Chapter Five. The special software developed by the author for statistical hypotheses testing with CBM (along with other known methods) is described in Chapter Six. The detailed experimental investigation of the statistical hypotheses testing methods developed on the basis of CBM and the results of their comparison with other known methods are given in Chapter Seven. The formalizations of absolutely different problems of human activity such as hypotheses testing problems in the solution of which the author was engaged in different periods of his life and some additional information about CBM are given in the appendices. Finally, it should be noted that, for understanding the materials given in the book, the knowledge of the basics of the probability theory and mathematical statistics is necessary. I think that this book will be useful for undergraduate and postgraduate students in the field of mathematics, mathematical statistics, applied statistics and other subfields for studying the modern methods of statistics and their application in research. It will also be useful for researchers and practitioners in the areas of hypotheses testing, as well as the estimation theory who develop these new methods and apply them to the solutions of different problems.

Cuprins

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