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In: Mathematics and statistics
Front Matter -- Structural Equation Modeling -- Structural Equation Modeling Software -- Steps in Structural Equation Modeling -- Advanced Topics: Principles and Applications -- References -- Index -- Other titles from iSTE in Mathematics and Statistics
1. Introducing Informal Inequality Measures (IIMs) Constructed from U-statistics of Degree Three or Higher in Analyzing Economic Disparity. 2. The Decomposition of the Gini Index Between and Within Groups: A Key Factor in Gender Studies. 3. A Note on the Decomposition of Health Inequality by Population Subgroups in the Case of Ordinal Variables. 4. The Gini index decomposition and the overlapping between population subgroups. 5. Gini's Mean Difference Based Minimum Risk Point Estimator of Mean. 6. The Gini concentration index for the study of survival. 7. An Axiomatic Analysis of Air Quality Assessment. 8. Sequential Interval and Point Estimation of Gini Index by Controlling Accuracies Relative to the Mean. 9. A Test on Correlation based on Gini's Mean Difference. 10. Multi-group Segregation for Nominal and Ordinal Categorical Data. 11. Exploring Fixed-Accuracy Estimation for Population Gini Inequality Index Under Big Data: A Passage to Practical Distribution-Free Strategies.
In: Chapman & Hall/CRC the R series
In: De Gruyter series in probability and stochastics volume 2
Frontmatter -- Introduction -- Contents -- Abbreviations and notations -- 1 Financial markets. From discrete to continuous time -- 2 Rate of convergence of asset and option prices -- 3 Limit theorems for markets with non-random time-varying coefficients -- 4 Convergence of stochastic integrals in application to financial markets -- A Essentials of calculus, probability, and stochastic processes -- Bibliography -- Index
Frontmatter -- Preface -- Contents -- Introduction -- Chapter I. Elements of Probability Theory -- Chapter II. Adaptation of Probabilistic Models -- Chapter III. Stochastic Oscillatory Processes -- Chapter IV. Modelling of Economic Cycles -- Chapter V. Features of Estimation Procedure -- Summary -- References -- Index
In: Statistics in practice
"More than 300 exercises at the end of each chapter provide the opportunity for readers to apply new concepts and test their knowledge. Answers for selected exercises (at the rear of the book) offer additional insights to help readers consolidate their understanding"--
In: Wiley series in probability and statistics
In: Wiley series in survey methodology
"This handbook provides technical guidance on statistical disclosure control and on how to approach the problem of balancing the need to provide users with statistical outputs and the need to protect the confidentiality of respondents. Statistical disclosure control is combined with other tools such as administrative, legal and IT in order to define a proper data dissemination strategy based on a risk management approach. The key concepts of statistical disclosure control are presented, along with the methodology and software that can be used to apply various methods of statistical disclosure control. Examples will also be used to illustrate methods described in the book. The handbook is based upon material prepared by the leading National Institute of Statistics in Europe. The context is relevant globally, not just within the EU."--
Thinking statistically about crime -- Homicide -- Police statistics -- National crime victimization survey -- Sampling principles and the ncvs -- NCVS measurement and missing data -- Judging the quality of a statistic -- Sexual assault -- Fraud and identity theft -- Big data and crime statistics -- Crime statistics, 1915 and beyond.
The R Companion to Elementary Applied Statistics includes traditional applications covered in elementary statistics courses as well as some additional methods that address questions that might arise during or after the application of commonly used methods. Beginning with basic tasks and computations with R, readers are then guided through ways to bring data into R, manipulate the data as needed, perform common statistical computations and elementary exploratory data analysis tasks, prepare customized graphics, and take advantage of R for a wide range of methods that find use in many elementary applications of statistics. Features: Requires no familiarity with R or programming to begin using this book. Can be used as a resource for a project-based elementary applied statistics course, or for researchers and professionals who wish to delve more deeply into R. Contains an extensive array of examples that illustrate ideas on various ways to use pre-packaged routines, as well as on developing individualized code. Presents quite a few methods that may be considered non-traditional, or advanced. Includes accompanying carefully documented script files that contain code for all examples presented, and more. R is a powerful and free product that is gaining popularity across the scientific community in both the professional and academic arenas. Statistical methods discussed in this book are used to introduce the fundamentals of using R functions and provide ideas for developing further skills in writing R code. These ideas are illustrated through an extensive collection of examples. About the Author: Christopher Hay-Jahans received his Doctor of Arts in mathematics from Idaho State University in 1999. After spending three years at University of South Dakota, he moved to Juneau, Alaska, in 2002 where he has taught a wide range of undergraduate courses at University of Alaska Southeast.
In: Chapman & Hall/CRC biostatistics series
Cost-effectiveness analysis is becoming an increasingly important tool for decision making in the health systems. Cost-Effectiveness of Medical Treatments formulates the cost-effectiveness analysis as a statistical decision problem, identifies the sources of uncertainty of the problem, and gives an overview of the frequentist and Bayesian statistical approaches for decision making. Basic notions on decision theory such as space of decisions, space of nature, utility function of a decision and optimal decisions, are explained in detail using easy to read mathematics. Features Focuses on cost-effectiveness analysis as a statistical decision problem and applies the well-established optimal statistical decision methodology. Discusses utility functions for cost-effectiveness analysis. Enlarges the class of models typically used in cost-effectiveness analysis with the incorporation of linear models to account for covariates of the patients. This permits the formulation of the group (or subgroup) theory. Provides Bayesian procedures to account for model uncertainty in variable selection for linear models and in clustering for models for heterogeneous data. Model uncertainty in cost-effectiveness analysis has not been considered in the literature. Illustrates examples with real data. In order to facilitate the practical implementation of real datasets, provides the codes in Mathematica for the proposed methodology. The motivation for the book is to make the achievements in cost-effectiveness analysis accessible to health providers, who need to make optimal decisions, to the practitioners and to the students of health sciences. Elaias Moreno is Professor of Statistics and Operational Research at the University of Granada, Spain, Corresponding Member of the Royal Academy of Sciences of Spain, and elect member of ISI. Francisco Josae Vaazquez-Polo is Professor of Mathematics and Bayesian Methods at the University of Las Palmas de Gran Canaria, and Head of the Department of Quantitative Methods. Miguel aAngel Negrain is Senior Lecturer in the Department of Quantitative Methods at the ULPGC. His main research topics are Bayesian methods applied to Health Economics, economic evaluation and cost-effectiveness analysis, meta-analysis and equity in the provision of healthcare services.
"There are a growing number of researchers and analysts who find the probability-based approaches for assessing risk and uncertainties to be too narrow and limiting. Uncertainty in Risk Assessment provides a broad conceptual framework and describes various alternative approaches of uncertainty representation and characterization therein such as probability-bound analysis, imprecise probability and evidence theory. The authors, whose own research has been at the forefront of developments in the field, include a number of real-life applications which demonstrate the practical use of the various methods in the different realistic circumstances. They provide invaluable practical guidance and clear recommendations on how and when to use the various approaches"--
In: Chapman & Hall/CRC series in operations research