Bias Reduction In Exponential Family Nonlinear Models
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Bias Reduction In Exponential Family Nonlinear Models
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Author :
language : en
Publisher:
Release Date : 2007
Bias Reduction In Exponential Family Nonlinear Models 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 categories.
An Introduction To Bartlett Correction And Bias Reduction
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Author : Gauss M. Cordeiro
language : en
Publisher: Springer Science & Business Media
Release Date : 2014-05-08
An Introduction To Bartlett Correction And Bias Reduction written by Gauss M. Cordeiro 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 2014-05-08 with Mathematics categories.
This book presents a concise introduction to Bartlett and Bartlett-type corrections of statistical tests and bias correction of point estimators. The underlying idea behind both groups of corrections is to obtain higher accuracy in small samples. While the main focus is on corrections that can be analytically derived, the authors also present alternative strategies for improving estimators and tests based on bootstrap, a data resampling technique and discuss concrete applications to several important statistical models.
Trends And Challenges In Categorical Data Analysis
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Author : Maria Kateri
language : en
Publisher: Springer Nature
Release Date : 2023-07-08
Trends And Challenges In Categorical Data Analysis written by Maria Kateri and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-08 with Mathematics categories.
This book provides a selection of modern and sophisticated methodologies for the analysis of large and complex univariate and multivariate categorical data. It gives an overview of a substantive and broad collection of topics in the analysis of categorical data, including association, marginal and graphical models, time series and fixed effects models, as well as modern methods of estimation such as regularization, Bayesian estimation and bias reduction methods, along with new simple measures for model interpretability. Methodological innovations and developments are illustrated and explained through real-world applications, together with useful R packages, allowing readers to replicate most of the analyses using the provided code. The applications span a variety of disciplines, including education, psychology, health, economics, and social sciences.
Categorical And Nonparametric Data Analysis
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Author : E. Michael Nussbaum
language : en
Publisher: Taylor & Francis
Release Date : 2024-05-30
Categorical And Nonparametric Data Analysis written by E. Michael Nussbaum and has been published by Taylor & Francis this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-30 with Psychology categories.
Now in its second edition, this book provides a focused, comprehensive overview of both categorical and nonparametric statistics, offering a conceptual framework for choosing the most appropriate test in various scenarios. The book’s clear explanations and Exploring the Concept boxes help reduce reader anxiety. Problems inspired by actual studies provide meaningful illustrations of these techniques. Basic statistics and probability are reviewed for those needing a refresher with mathematical derivations placed in optional appendices. Highlights include the following: • Three chapters co-authored with Edgar Brunner address modern nonparametric techniques, along with accompanying R code. • Unique coverage of both categorical and nonparametric statistics better prepares readers to select the best technique for particular research projects. • Designed to be used with most statistical packages, clear examples of how to use the tests in SPSS, R, and Excel foster conceptual understanding. • Exploring the Concept boxes integrated throughout prompt students to draw links between the concepts to deepen understanding. • Fully developed Instructor and Student Resources featuring datasets for the book's problems and a guide to R, and for the instructor PowerPoints, author's syllabus, and answers to even-numbered problems. Intended for graduate or advanced undergraduate courses in categorical and nonparametric statistics taught in psychology, education, human development, sociology, political science, and other social and life sciences.
Design Of Experiments For Generalized Linear Models
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Author : Kenneth G. Russell
language : en
Publisher: CRC Press
Release Date : 2018-12-14
Design Of Experiments For Generalized Linear Models written by Kenneth G. Russell and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-14 with Mathematics categories.
Generalized Linear Models (GLMs) allow many statistical analyses to be extended to important statistical distributions other than the Normal distribution. While numerous books exist on how to analyse data using a GLM, little information is available on how to collect the data that are to be analysed in this way. This is the first book focusing specifically on the design of experiments for GLMs. Much of the research literature on this topic is at a high mathematical level, and without any information on computation. This book explains the motivation behind various techniques, reduces the difficulty of the mathematics, or moves it to one side if it cannot be avoided, and gives examples of how to write and run computer programs using R. Features The generalisation of the linear model to GLMs Background mathematics, and the use of constrained optimisation in R Coverage of the theory behind the optimality of a design Individual chapters on designs for data that have Binomial or Poisson distributions Bayesian experimental design An online resource contains R programs used in the book This book is aimed at readers who have done elementary differentiation and understand minimal matrix algebra, and have familiarity with R. It equips professional statisticians to read the research literature. Nonstatisticians will be able to design their own experiments by following the examples and using the programs provided.
Vector Generalized Linear And Additive Models
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Author : Thomas W. Yee
language : en
Publisher: Springer
Release Date : 2015-09-11
Vector Generalized Linear And Additive Models written by Thomas W. Yee and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-09-11 with Mathematics categories.
This book presents a greatly enlarged statistical framework compared to generalized linear models (GLMs) with which to approach regression modelling. Comprising of about half-a-dozen major classes of statistical models, and fortified with necessary infrastructure to make the models more fully operable, the framework allows analyses based on many semi-traditional applied statistics models to be performed as a coherent whole. Since their advent in 1972, GLMs have unified important distributions under a single umbrella with enormous implications. However, GLMs are not flexible enough to cope with the demands of practical data analysis. And data-driven GLMs, in the form of generalized additive models (GAMs), are also largely confined to the exponential family. The methodology here and accompanying software (the extensive VGAM R package) are directed at these limitations and are described comprehensively for the first time in one volume. This book treats distributions and classical models as generalized regression models, and the result is a much broader application base for GLMs and GAMs. The book can be used in senior undergraduate or first-year postgraduate courses on GLMs or categorical data analysis and as a methodology resource for VGAM users. In the second part of the book, the R package VGAM allows readers to grasp immediately applications of the methodology. R code is integrated in the text, and datasets are used throughout. Potential applications include ecology, finance, biostatistics, and social sciences. The methodological contribution of this book stands alone and does not require use of the VGAM package.
Supervised Machine Learning
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Author : Tanya Kolosova
language : en
Publisher: CRC Press
Release Date : 2020-09-21
Supervised Machine Learning written by Tanya Kolosova and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-21 with Computers categories.
AI framework intended to solve a problem of bias-variance tradeoff for supervised learning methods in real-life applications. The AI framework comprises of bootstrapping to create multiple training and testing data sets with various characteristics, design and analysis of statistical experiments to identify optimal feature subsets and optimal hyper-parameters for ML methods, data contamination to test for the robustness of the classifiers. Key Features: Using ML methods by itself doesn’t ensure building classifiers that generalize well for new data Identifying optimal feature subsets and hyper-parameters of ML methods can be resolved using design and analysis of statistical experiments Using a bootstrapping approach to massive sampling of training and tests datasets with various data characteristics (e.g.: contaminated training sets) allows dealing with bias Developing of SAS-based table-driven environment allows managing all meta-data related to the proposed AI framework and creating interoperability with R libraries to accomplish variety of statistical and machine-learning tasks Computer programs in R and SAS that create AI framework are available on GitHub
Making Financial Globalization
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Author : Clara Park
language : en
Publisher: Oxford University Press
Release Date : 2024-10-29
Making Financial Globalization written by Clara Park and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-29 with Political Science categories.
In Making Financial Globalization, Clara Park challenges the conventional wisdom that finance has always been global. Drawing on original datasets of financial trade restrictions and domestic financial regulations in over 100 countries, archival research of international negotiations, and case studies of the US and China, Park details how financial firms used multilateral lobbying strategies to create an international framework for financial service liberalization. As she shows, the powerful coalition across industries and countries exerted considerable pressure on national governments, who had to weigh the costs and benefits of liberalization, and facilitated international negotiations. A novel political-economic explanation for financial globalization, this timely book challenges state-centric views in international relations and emphasizes the interplay of firms and politics as a central factor shaping financial globalization.
Machine Learning In Medicine A Complete Overview
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Author : Ton J. Cleophas
language : en
Publisher: Springer Nature
Release Date : 2020-03-03
Machine Learning In Medicine A Complete Overview written by Ton J. Cleophas and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-03 with Medical categories.
Adequate health and health care is no longer possible without proper data supervision from modern machine learning methodologies like cluster models, neural networks, and other data mining methodologies. The current book is the first publication of a complete overview of machine learning methodologies for the medical and health sector, and it was written as a training companion, and as a must-read, not only for physicians and students, but also for any one involved in the process and progress of health and health care. In this second edition the authors have removed the textual errors from the first edition. Also, the improved tables from the first edition, have been replaced with the original tables from the software programs as applied. This is, because, unlike the former, the latter were without error, and readers were better familiar with them. The main purpose of the first edition was, to provide stepwise analyses of the novel methods from data examples, but background information and clinical relevance information may have been somewhat lacking. Therefore, each chapter now contains a section entitled "Background Information". Machine learning may be more informative, and may provide better sensitivity of testing than traditional analytic methods may do. In the second edition a place has been given for the use of machine learning not only to the analysis of observational clinical data, but also to that of controlled clinical trials. Unlike the first edition, the second edition has drawings in full color providing a helpful extra dimension to the data analysis. Several machine learning methodologies not yet covered in the first edition, but increasingly important today, have been included in this updated edition, for example, negative binomial and Poisson regressions, sparse canonical analysis, Firth's bias adjusted logistic analysis, omics research, eigenvalues and eigenvectors.
Quasi Likelihood And Its Application
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Author : Christopher C. Heyde
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-01-08
Quasi Likelihood And Its Application written by Christopher C. Heyde 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 2008-01-08 with Mathematics categories.
The first account in book form of all the essential features of the quasi-likelihood methodology, stressing its value as a general purpose inferential tool. The treatment is rather informal, emphasizing essential principles rather than detailed proofs, and readers are assumed to have a firm grounding in probability and statistics at the graduate level. Many examples of the use of the methods in both classical statistical and stochastic process contexts are provided.