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"Statistical methodology itself has made some significant developments in areas that are highly relevant to the problems faced by environmentalists; thus this book fills a gap in the market in which there is currently a lot of interest. Split into two parts, part 1 - Theory and methods - introduces the basis for and scope of the book, and covers amongst others the chief topics of exploratory analysis, non-parametric estimation and testing, and parametric modeling. Part 2 - Case Studies - introduces a number of co-authors, specialists in their own areas of environmental science, to illustrate the application of the theory and methods in practice. The accompanying website develops the practical aspects raised in the book, and provides a useful complementary tool."--Provided by publisher

Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Dedication -- Contents -- Preface -- What Are the Aims of the Book? -- What Are the Key Features of the Book? -- The Structure of the Book -- Acknowledgements -- Part I Fundamentals for Modelling Spatial and Spatial-Temporal Data -- 1 Challenges and Opportunities Analysing Spatial and Spatial-Temporal Data -- 1.1 Introduction -- 1.2 Four Main Challenges When Analysing Spatial and Spatial-Temporal Data -- 1.2.1 Dependency -- 1.2.2 Heterogeneity -- 1.2.3 Data Sparsity -- 1.2.4 Uncertainty -- 1.2.4.1 Data Uncertainty

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.

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

"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"--

"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.