Statistical Planning And Inference

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Statistical Planning And Inference

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Author : Subir Ghosh
language : en
Publisher: Wiley
Release Date : 2021-10-25

Statistical Planning And Inference written by Subir Ghosh and has been published by Wiley this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-25 with Science categories.

This book introduces statistical planning and inference, presenting both classical theory and the major developments in the field. Each chapter presents problems and their solutions along with illustrative examples to introduce concepts and methods, and is supported by a supplementary website featuring guidance on how to implement methods using R.

Journal Of Statistical Planning And Inference

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Author : North-Holland Publishing Company
language : en
Publisher:
Release Date : 2003

Journal Of Statistical Planning And Inference written by North-Holland Publishing Company and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with categories.

Statistical Inference In Stochastic Processes

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Author : Ishwar V. Basawa
language : en
Publisher:
Release Date : 1994

Statistical Inference In Stochastic Processes written by Ishwar V. Basawa and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with categories.

Selected Works Of Debabrata Basu

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Author : Anirban DasGupta
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-02-04

Selected Works Of Debabrata Basu written by Anirban DasGupta and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-02-04 with Mathematics categories.

This book contains a little more than 20 of Debabrata Basu's most significant articles and writings. Debabrata Basu is internationally known for his highly influential and fundamental contributions to the foundations of statistics, survey sampling, sufficiency, and invariance. The major theorem bearing his name has had numerous applications to statistics and probability. The articles in this volume are reprints of the original articles, in a chronological order. The book also contains eleven commentaries written by some of the most distinguished scholars in the area of foundations and statistical inference. These commentaries are by George Casella and V. Gopal, Phil Dawid, Tom DiCiccio and Alastair Young, Malay Ghosh, Jay kadane, Glen Meeden, Robert Serfling, Jayaram Sethuraman, Terry Speed, and Alan Welsh.

Probability For Statistics And Machine Learning

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Author : Anirban DasGupta
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-05-17

Probability For Statistics And Machine Learning written by Anirban DasGupta and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-05-17 with Mathematics categories.

This book provides a versatile and lucid treatment of classic as well as modern probability theory, while integrating them with core topics in statistical theory and also some key tools in machine learning. It is written in an extremely accessible style, with elaborate motivating discussions and numerous worked out examples and exercises. The book has 20 chapters on a wide range of topics, 423 worked out examples, and 808 exercises. It is unique in its unification of probability and statistics, its coverage and its superb exercise sets, detailed bibliography, and in its substantive treatment of many topics of current importance. This book can be used as a text for a year long graduate course in statistics, computer science, or mathematics, for self-study, and as an invaluable research reference on probabiliity and its applications. Particularly worth mentioning are the treatments of distribution theory, asymptotics, simulation and Markov Chain Monte Carlo, Markov chains and martingales, Gaussian processes, VC theory, probability metrics, large deviations, bootstrap, the EM algorithm, confidence intervals, maximum likelihood and Bayes estimates, exponential families, kernels, and Hilbert spaces, and a self contained complete review of univariate probability.

Probabilistic Foundations Of Statistical Network Analysis

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Author : Harry Crane
language : en
Publisher: CRC Press
Release Date : 2018-04-17

Probabilistic Foundations Of Statistical Network Analysis written by Harry Crane and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-04-17 with Business & Economics categories.

Probabilistic Foundations of Statistical Network Analysis presents a fresh and insightful perspective on the fundamental tenets and major challenges of modern network analysis. Its lucid exposition provides necessary background for understanding the essential ideas behind exchangeable and dynamic network models, network sampling, and network statistics such as sparsity and power law, all of which play a central role in contemporary data science and machine learning applications. The book rewards readers with a clear and intuitive understanding of the subtle interplay between basic principles of statistical inference, empirical properties of network data, and technical concepts from probability theory. Its mathematically rigorous, yet non-technical, exposition makes the book accessible to professional data scientists, statisticians, and computer scientists as well as practitioners and researchers in substantive fields. Newcomers and non-quantitative researchers will find its conceptual approach invaluable for developing intuition about technical ideas from statistics and probability, while experts and graduate students will find the book a handy reference for a wide range of new topics, including edge exchangeability, relative exchangeability, graphon and graphex models, and graph-valued Levy process and rewiring models for dynamic networks. The author’s incisive commentary supplements these core concepts, challenging the reader to push beyond the current limitations of this emerging discipline. With an approachable exposition and more than 50 open research problems and exercises with solutions, this book is ideal for advanced undergraduate and graduate students interested in modern network analysis, data science, machine learning, and statistics. Harry Crane is Associate Professor and Co-Director of the Graduate Program in Statistics and Biostatistics and an Associate Member of the Graduate Faculty in Philosophy at Rutgers University. Professor Crane’s research interests cover a range of mathematical and applied topics in network science, probability theory, statistical inference, and mathematical logic. In addition to his technical work on edge and relational exchangeability, relative exchangeability, and graph-valued Markov processes, Prof. Crane’s methods have been applied to domain-specific cybersecurity and counterterrorism problems at the Foreign Policy Research Institute and RAND’s Project AIR FORCE.

Journal Of Statistical Planning And Inference

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Author :
language : en
Publisher:
Release Date : 1994

Journal Of Statistical Planning And Inference written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Mathematical statistics categories.

Journal Of Statistical Planning And Inference

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Author : Elsevier Science (Firm)
language : en
Publisher:
Release Date : 2002

Journal Of Statistical Planning And Inference written by Elsevier Science (Firm) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with categories.

Statistical Inference

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Author : Michael J. Panik
language : en
Publisher: John Wiley & Sons
Release Date : 2012-06-06

Statistical Inference written by Michael J. Panik and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-06-06 with Mathematics categories.

A concise, easily accessible introduction to descriptive and inferential techniques Statistical Inference: A Short Course offers a concise presentation of the essentials of basic statistics for readers seeking to acquire a working knowledge of statistical concepts, measures, and procedures. The author conducts tests on the assumption of randomness and normality, provides nonparametric methods when parametric approaches might not work. The book also explores how to determine a confidence interval for a population median while also providing coverage of ratio estimation, randomness, and causality. To ensure a thorough understanding of all key concepts, Statistical Inference provides numerous examples and solutions along with complete and precise answers to many fundamental questions, including: How do we determine that a given dataset is actually a random sample? With what level of precision and reliability can a population sample be estimated? How are probabilities determined and are they the same thing as odds? How can we predict the level of one variable from that of another? What is the strength of the relationship between two variables? The book is organized to present fundamental statistical concepts first, with later chapters exploring more advanced topics and additional statistical tests such as Distributional Hypotheses, Multinomial Chi-Square Statistics, and the Chi-Square Distribution. Each chapter includes appendices and exercises, allowing readers to test their comprehension of the presented material. Statistical Inference: A Short Course is an excellent book for courses on probability, mathematical statistics, and statistical inference at the upper-undergraduate and graduate levels. The book also serves as a valuable reference for researchers and practitioners who would like to develop further insights into essential statistical tools.

Statistical Inference

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Author : S.D. Silvey
language : en
Publisher: CRC Press
Release Date : 1975-03-01

Statistical Inference written by S.D. Silvey and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1975-03-01 with Mathematics categories.

Minimum-variance unbiased estimation; The method of least squares; The method of maximum likelihood; Confidence sets; Hypothesis testing; The likelihood-ratio test and alternative 'large-sample' equivalents of it 108; Sequential tests; Non-parametric methods; The bayesian approach; An introduction to decision theory.