Randomness And Optimal Estimation In Data Sampling
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Randomness And Optimal Estimation In Data Sampling
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Author : Dr. Jack Allen
language : en
Publisher:
Release Date : 2002-01-01
Randomness And Optimal Estimation In Data Sampling written by Dr. Jack Allen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002-01-01 with Mathematics categories.
Randomness And Optimal Estimation In Data Sampling
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Author : M. Khoshnevisan, S. Saxena, H. P. Singh, S. Singh, F. Smarandache
language : en
Publisher: Infinite Study
Release Date : 2007
Randomness And Optimal Estimation In Data Sampling written by M. Khoshnevisan, S. Saxena, H. P. Singh, S. Singh, F. Smarandache and has been published by Infinite Study this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Estimation theory categories.
Randomness And Optimal Estimation In Data Sampling
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Author :
language : en
Publisher:
Release Date : 2007
Randomness And Optimal Estimation In Data Sampling written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Estimation theory categories.
Data Analysis Of Medical Studies
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Author : Potter C. Chang
language : en
Publisher: Springer Nature
Release Date : 2025-07-22
Data Analysis Of Medical Studies written by Potter C. Chang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-22 with Mathematics categories.
This book is written for the many health professionals who are regularly frustrated by elegant but ambiguous descriptions of results of data analysis. It uses articles of the New England Journal of Medicine to demonstrate how ambiguous descriptions of results of data analysis may be read, so that it is clear what they do and do not reveal. These demonstrations also show how statistics is misused.
Introduction To Bayesian Statistics
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Author : William M. Bolstad
language : en
Publisher: John Wiley & Sons
Release Date : 2013-06-05
Introduction To Bayesian Statistics written by William M. Bolstad 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 2013-06-05 with Mathematics categories.
Praise for the First Edition "I cannot think of a better book for teachers of introductory statistics who want a readable and pedagogically sound text to introduce Bayesian statistics." —Statistics in Medical Research "[This book] is written in a lucid conversational style, which is so rare in mathematical writings. It does an excellent job of presenting Bayesian statistics as a perfectly reasonable approach to elementary problems in statistics." —STATS: The Magazine for Students of Statistics, American Statistical Association "Bolstad offers clear explanations of every concept and method making the book accessible and valuable to undergraduate and graduate students alike." —Journal of Applied Statistics The use of Bayesian methods in applied statistical analysis has become increasingly popular, yet most introductory statistics texts continue to only present the subject using frequentist methods. Introduction to Bayesian Statistics, Second Edition focuses on Bayesian methods that can be used for inference, and it also addresses how these methods compare favorably with frequentist alternatives. Teaching statistics from the Bayesian perspective allows for direct probability statements about parameters, and this approach is now more relevant than ever due to computer programs that allow practitioners to work on problems that contain many parameters. This book uniquely covers the topics typically found in an introductory statistics book—but from a Bayesian perspective—giving readers an advantage as they enter fields where statistics is used. This Second Edition provides: Extended coverage of Poisson and Gamma distributions Two new chapters on Bayesian inference for Poisson observations and Bayesian inference for the standard deviation for normal observations A twenty-five percent increase in exercises with selected answers at the end of the book A calculus refresher appendix and a summary on the use of statistical tables New computer exercises that use R functions and Minitab® macros for Bayesian analysis and Monte Carlo simulations Introduction to Bayesian Statistics, Second Edition is an invaluable textbook for advanced undergraduate and graduate-level statistics courses as well as a practical reference for statisticians who require a working knowledge of Bayesian statistics.
Combining Modelling And Analyzing Imprecision Randomness And Dependence
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Author : Jonathan Ansari
language : en
Publisher: Springer Nature
Release Date : 2024-08-09
Combining Modelling And Analyzing Imprecision Randomness And Dependence written by Jonathan Ansari and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-09 with Computers categories.
This volume contains more than 65 peer-reviewed papers corresponding to presentations at the 11th Conference on Soft Methods in Probability and Statistics (SMPS) held in Salzburg, Austria, in September 2024. It covers recent advances in the field of probability, statistics, and data science, with a particular focus on dealing with dependence, imprecision and incomplete information. Reflecting the fact that data science continues to evolve, this book serves as a bridge between different groups of experts, including statisticians, mathematicians, computer scientists, and engineers, and encourages interdisciplinary research. The selected contributions cover a wide range of topics such as imprecise probabilities, random sets, belief functions, possibility theory, and dependence modeling. Readers will find discussions on clustering, depth concepts, dimensionality reduction, and robustness, reflecting the conference's commitment to addressing real-world challenges through innovative methods.
Introduction To Random Signals Estimation Theory And Kalman Filtering
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Author : M. Sami Fadali
language : en
Publisher: Springer Nature
Release Date : 2024-04-01
Introduction To Random Signals Estimation Theory And Kalman Filtering written by M. Sami Fadali and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-01 with Technology & Engineering categories.
This book provides first-year graduate engineering students and practicing engineers with a solid introduction to random signals and estimation. It includes a statistical background that is often omitted in other textbooks but is essential for a clear understanding of estimators and their properties. The book emphasizes applicability rather than mathematical theory. It includes many examples and exercises to demonstrate and learn the theory that makes extensive use of MATLAB and its toolboxes. Although there are several excellent books on random signals and Kalman filtering, this book fulfills the need for a book that is suitable for a single-semester course that covers both random signals and Kalman filters and is used for a two-semester course for students that need remedial background. For students interested in more advanced studies in the area, the book provides a bridge between typical undergraduate engineering education and more advanced graduate-level courses.
Randomness And Realism Encounters With Randomness In The Scientific Search For Physical Reality
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Author : John W Fowler
language : en
Publisher: World Scientific
Release Date : 2021-07-08
Randomness And Realism Encounters With Randomness In The Scientific Search For Physical Reality written by John W Fowler and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-08 with Science categories.
Randomness is an active element relevant to all scientific activities. The book explores the way in which randomness suffuses the human experience, starting with everyday chance events, followed by developments into modern probability theory, statistical mechanics, scientific data analysis, quantum mechanics, and quantum gravity. An accessible introduction to these theories is provided as a basis for going into deeper topics.Fowler unveils the influence of randomness in the two pillars of science, measurement and theory. Some emphasis is placed on the need and methods for optimal characterization of uncertainty. An example of the cost of neglecting this is the St. Petersburg Paradox, a theoretical game of chance with an infinite expected payoff value. The role of randomness in quantum mechanics reveals another particularly interesting finding: that in order for the physical universe to function as it does and permit conscious beings within it to enjoy sanity, irreducible randomness is necessary at the quantum level.The book employs a certain level of mathematics to describe physical reality in a more precise way that avoids the tendency of nonmathematical descriptions to be occasionally misleading. Thus, it is most readily digested by young students who have taken at least a class in introductory calculus, or professional scientists and engineers curious about the book's topics as a result of hearing about them in popular media. Readers not inclined to savor equations should be able to skip certain technical sections without losing the general flow of ideas. Still, it is hoped that even readers who usually avoid equations will give those within these pages a chance, as they may be surprised at how potentially foreboding concepts fall into line when one makes a legitimate attempt to follow a succession of mathematical implications.
An Introduction To Multilevel Modeling Techniques
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Author : Ronald H. Heck
language : en
Publisher: Routledge
Release Date : 2015-03-05
An Introduction To Multilevel Modeling Techniques written by Ronald H. Heck and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-03-05 with Psychology categories.
Univariate and multivariate multilevel models are used to understand how to design studies and analyze data in this comprehensive text distinguished by its variety of applications from the educational, behavioral, and social sciences. Basic and advanced models are developed from the multilevel regression (MLM) and latent variable (SEM) traditions within one unified analytic framework for investigating hierarchical data. The authors provide examples using each modeling approach and also explore situations where alternative approaches may be more appropriate, given the research goals. Numerous examples and exercises allow readers to test their understanding of the techniques presented. Changes to the new edition include: -The use of Mplus 7.2 for running the analyses including the input and data files at www.routledge.com/9781848725522. -Expanded discussion of MLM and SEM model-building that outlines the steps taken in the process, the relevant Mplus syntax, and tips on how to evaluate the models. -Expanded pedagogical program now with chapter objectives, boldfaced key terms, a glossary, and more tables and graphs to help students better understand key concepts and techniques. -Numerous, varied examples developed throughout which make this book appropriate for use in education, psychology, business, sociology, and the health sciences. -Expanded coverage of missing data problems in MLM using ML estimation and multiple imputation to provide currently-accepted solutions (Ch. 10). -New chapter on three-level univariate and multilevel multivariate MLM models provides greater options for investigating more complex theoretical relationships(Ch.4). -New chapter on MLM and SEM models with categorical outcomes facilitates the specification of multilevel models with observed and latent outcomes (Ch.8). -New chapter on multilevel and longitudinal mixture models provides readers with options for identifying emergent groups in hierarchical data (Ch.9). -New chapter on the utilization of sample weights, power analysis, and missing data provides guidance on technical issues of increasing concern for research publication (Ch.10). Ideal as a text for graduate courses on multilevel, longitudinal, latent variable modeling, multivariate statistics, or advanced quantitative techniques taught in psychology, business, education, health, and sociology, this book’s practical approach also appeals to researchers. Recommended prerequisites are introductory univariate and multivariate statistics.
Sampling And Estimation From Finite Populations
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Author : Yves Tille
language : en
Publisher: John Wiley & Sons
Release Date : 2020-03-30
Sampling And Estimation From Finite Populations written by Yves Tille 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 2020-03-30 with Mathematics categories.
A much-needed reference on survey sampling and its applications that presents the latest advances in the field Seeking to show that sampling theory is a living discipline with a very broad scope, this book examines the modern development of the theory of survey sampling and the foundations of survey sampling. It offers readers a critical approach to the subject and discusses putting theory into practice. It also explores the treatment of non-sampling errors featuring a range of topics from the problems of coverage to the treatment of non-response. In addition, the book includes real examples, applications, and a large set of exercises with solutions. Sampling and Estimation from Finite Populations begins with a look at the history of survey sampling. It then offers chapters on: population, sample, and estimation; simple and systematic designs; stratification; sampling with unequal probabilities; balanced sampling; cluster and two-stage sampling; and other topics on sampling, such as spatial sampling, coordination in repeated surveys, and multiple survey frames. The book also includes sections on: post-stratification and calibration on marginal totals; calibration estimation; estimation of complex parameters; variance estimation by linearization; and much more. Provides an up-to-date review of the theory of sampling Discusses the foundation of inference in survey sampling, in particular, the model-based and design-based frameworks Reviews the problems of application of the theory into practice Also deals with the treatment of non sampling errors Sampling and Estimation from Finite Populations is an excellent book for methodologists and researchers in survey agencies and advanced undergraduate and graduate students in social science, statistics, and survey courses.