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Selected Topics in Statistical Inference

Autor Manisha Pal, Bikas K. Sinha
en Limba Engleză Hardback – 12 sep 2024
This book focuses exclusively on the domain of parametric inference and that, too, from a reader’s perspective, i.e., covering only point estimation of parameter(s). It covers those topics in parametric inference which need clarity of exposure to students, researchers, and teachers alike; mere statements of theorems and proofs may not always reveal the inner beauty and significance of some aspects of inference. To ensure clarity, the book discusses the following topics at an advanced level—(1) sequential (unbiased) point estimation of ‘p’ and its functions; generalization to trinomial and tetranomial populations; (2) some aspects of the use of additional resources in finite population inference; (3) the concept of sufficiency vis-à-vis the notion of sufficient experiments and comparison of experiments; (4) estimation of the size of a finite population with special features; and (5) unbiased estimation of reliability in exponential samples and other settings. This book provides a platform for thought-provoking, creative, and challenging discussions on a variety of topics in statistical estimation theory, it is also ideal for research methodology course for statistics research scholars, and for clarification of basic ideas in topics discussed at basic/advanced levels.
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Specificații

ISBN-13: 9789819725915
ISBN-10: 9819725917
Pagini: 160
Ilustrații: X, 170 p.
Dimensiuni: 160 x 241 x 15 mm
Greutate: 0.36 kg
Ediția:2024
Editura: Springer
Locul publicării:Singapore, Singapore

Cuprins

Glimpses of the Book.- Sequential Binomial Estimation.- Use of Additional Resources in Finite Population Inference.- Notion of sufficiency in statistical inference - Theory and Applications.- Estimation of the Unknown Size of a Finite Population with Special Features.- Unbiased Estimation of Reliability in Exponential Samples.

Notă biografică

Manisha Pal ist eine emeritierte Professorin für Statistik an der Universität Kolkata, Indien, und derzeit Senior-Professorin am Fachbereich Statistik der St. Xavier’s University, Kolkata, Indien. Seit 1982 ist sie in erfolgreiche Forschungsarbeiten eingebunden und hat über 125 Forschungsarbeiten in referierten Fachzeitschriften veröffentlicht. Dabei arbeitete sie mit zahlreichen Forschern in Indien und im Ausland zusammen. Ihr gemeinsam verfasstes Buch „Optimal Mixture Experiments" wurde in der renommierten Buchreihe Lecture Notes in Statistics des Springer-Verlags veröffentlicht. Ihre Forschungsinteressen umfassen Lagerhaltungskontrolle, Zuverlässigkeitsinferenz, schiefe Verteilungen, Mischungsversuche und Datenanalyse.
Bikas K. Sinha ist ein emeritierter Professor für Statistik am Indian Statistical Institute in Kolkata. Er hat bedeutende Beiträge sowohl zur statistischen Theorie als auch zu deren Anwendungen geleistet. Er hat eine Reihe von Forschungsmonographien und Lehrbüchern bei international anerkannten Verlagen, darunter Springer, veröffentlicht. Mit mehr als 110 Forschungskollaborationen weltweit, über 160 Veröffentlichungen in referierten Fachzeitschriften und drei gemeinsam verfassten Forschungsmonographien, die in der Lecture Notes in Statistics-Reihe von Springer (1989, 2002, 2014) erschienen sind, haben ihm seine wissenschaftlichen Beiträge internationale akademische Anerkennung im Bereich Statistik eingebracht.

Textul de pe ultima copertă

This book focuses exclusively on the domain of parametric inference and that, too, from a reader’s perspective, i.e., covering only point estimation of parameter(s). It covers those topics in parametric inference which need clarity of exposure to students, researchers, and teachers alike; mere statements of theorems and proofs may not always reveal the inner beauty and significance of some aspects of inference. To ensure clarity, the book discusses the following topics at an advanced level—(1) sequential (unbiased) point estimation of ‘p’ and its functions; generalization to trinomial and tetranomial populations; (2) some aspects of the use of additional resources in finite population inference; (3) the concept of sufficiency vis-à-vis the notion of sufficient experiments and comparison of experiments; (4) estimation of the size of a finite population with special features; and (5) unbiased estimation of reliability in exponential samples and other settings. This book provides a platform for thought-provoking, creative, and challenging discussions on a variety of topics in statistical estimation theory, it is also ideal for research methodology course for statistics research scholars, and for clarification of basic ideas in topics discussed at basic/advanced levels.

Caracteristici

Addresses and explains basic issues in statistical inference using simple problems with far-reaching implications Exposes readers to some point estimation topics, focusing on reader’s perspective of domain of parametric inference Provides ample scope to a serious researcher for addressing challenging questions and developing research tools