Exercises and Solutions in Statistical Theory helps students and scientists obtain an in-depth understanding of statistical theory by working on and reviewing solutions to interesting and challenging exercises of practical importance. Unlike similar books, this text incorporates many exercises that apply to real-world settings and provides much more thorough solutions. The exercises and selected detailed solutions cover from basic probability theory through to the theory of statistical inference. Many of the exercises deal with important, real-life scenarios in areas such as medicine, epidemiology, actuarial science, social science, engineering, physics, chemistry, biology, environmental health, and sports. Several exercises illustrate the utility of study design strategies, sampling from finite populations, maximum likelihood, asymptotic theory, latent class analysis, conditional inference, regression analysis, generalized linear models, Bayesian analysis, and other statistical topics. The book also contains references to published books and articles that offer more information about the statistical concepts. Designed as a supplement for advanced undergraduate and graduate courses, this text is a valuable source of classroom examples, homework problems, and examination questions. It is also useful for scientists interested in enhancing or refreshing their theoretical statistical skills. The book improves readers’ comprehension of the principles of statistical theory and helps them see how the principles can be used in practice. By mastering the theoretical statistical strategies necessary to solve the exercises, readers will be prepared to successfully study even higher-level statistical theory.

Designed for a one-semester advanced undergraduate or graduate course, Statistical Theory: A Concise Introduction clearly explains the underlying ideas and principles of major statistical concepts, including parameter estimation, confidence intervals, hypothesis testing, asymptotic analysis, Bayesian inference, and elements of decision theory. It introduces these topics on a clear intuitive level using illustrative examples in addition to the formal definitions, theorems, and proofs. Based on the authors’ lecture notes, this student-oriented, self-contained book maintains a proper balance between the clarity and rigor of exposition. In a few cases, the authors present a "sketched" version of a proof, explaining its main ideas rather than giving detailed technical mathematical and probabilistic arguments. Chapters and sections marked by asterisks contain more advanced topics and may be omitted. A special chapter on linear models shows how the main theoretical concepts can be applied to the well-known and frequently used statistical tool of linear regression. Requiring no heavy calculus, simple questions throughout the text help students check their understanding of the material. Each chapter also includes a set of exercises that range in level of difficulty.

Drawn from nearly four decades of Lawrence L. Kupper’s teaching experiences as a distinguished professor in the Department of Biostatistics at the University of North Carolina, Exercises and Solutions in Biostatistical Theory presents theoretical statistical concepts, numerous exercises, and detailed solutions that span topics from basic probability to statistical inference. The text links theoretical biostatistical principles to real-world situations, including some of the authors’ own biostatistical work that has addressed complicated design and analysis issues in the health sciences. This classroom-tested material is arranged sequentially starting with a chapter on basic probability theory, followed by chapters on univariate distribution theory and multivariate distribution theory. The last two chapters on statistical inference cover estimation theory and hypothesis testing theory. Each chapter begins with an in-depth introduction that summarizes the biostatistical principles needed to help solve the exercises. Exercises range in level of difficulty from fairly basic to more challenging (identified with asterisks). By working through the exercises and detailed solutions in this book, students will develop a deep understanding of the principles of biostatistical theory. The text shows how the biostatistical theory is effectively used to address important biostatistical issues in a variety of real-world settings. Mastering the theoretical biostatistical principles described in the book will prepare students for successful study of higher-level statistical theory and will help them become better biostatisticians.

A Balanced Treatment of Bayesian and Frequentist Inference Statistical Inference: An Integrated Approach, Second Edition presents an account of the Bayesian and frequentist approaches to statistical inference. Now with an additional author, this second edition places a more balanced emphasis on both perspectives than the first edition. New to the Second Edition New material on empirical Bayes and penalized likelihoods and their impact on regression models Expanded material on hypothesis testing, method of moments, bias correction, and hierarchical models More examples and exercises More comparison between the approaches, including their similarities and differences Designed for advanced undergraduate and graduate courses, the text thoroughly covers statistical inference without delving too deep into technical details. It compares the Bayesian and frequentist schools of thought and explores procedures that lie on the border between the two. Many examples illustrate the methods and models, and exercises are included at the end of each chapter.

In the book there are introduced models and methods of construction of pseudo-solutions for the well-posed and ill-posed linear functional equations circumscribing models passive, active and complicated experiments. Two types of the functional equations are considered: systems of the linear algebraic equations and linear integral equations. Methods of construction of pseudos6lutions are developed in the presence of passive right-hand side errors for two types of operator errors: passive measurements and active representation errors of the operator, and all their combinations. For the determined and stochastic models of passive experiments the method of the least distances of construction of pseudosolutions is created, the maximum likelihood method of construction of pseudosolutions is applied for active experiments, and then methods for combinations of models of regression, of passive and of active experiments are created. We have constructed regularized variants of these methods for systems of the linear algebraic equations with the degenerated matrices and for linear integral equations of the first kind. In pure mathematics, the solution techniques of the functional equations with exact input data more often are studied. In applied mathematics, problem consists in construction of pseudosolutions, that is, solution of the hctional equations with perturbed input data. Such problem in many cases is incomparably more complicated. The book is devoted to a problem of construction of a pseudosolution (the problem of a parameter estimation) in the following fundamental sections of applied mathematics: confluent models passive, active and the every possible mixed experiments.

Most of the properties of a metal-electrolyte interface, even the spe cific nature of an electrode reaction, proneness of a metal to cor rosion, etc., are primarily determined by the electrical double layer (EDL) at this boundary. It is therefore no surprise that for the last, at least, one hundred years intent attention should have been centered on EDL. So much of material has been gathered to date that we are easi ly lost in this maze of information. A substantial part of the attempts to systematize these facts is made at present within the framework of thermodynamics. Such a confined approach is undoubtedly inadequate. The Gouy-Chapman theory and the Stern-Grahame model of the dense part of EDL developed 40-70 years ago, tailored appropriately to suit the occasion, inevitably underlie any description of EDL. This route is rather too narrow to explain all the facts at our disposal. A dire necessity has thus arisen for widening the principles of the micros copic theory. This is precisely the objective of our monograph. Fur thermore, we shall dwell at length on the comparison of the theory with experiment: without such a comparative analysis, any theory, however elegant it may be, is just an empty drum.

Exploring Modeling with Data and Differential Equations Using R provides a unique introduction to differential equations with applications to the biological and other natural sciences. Additionally, model parameterization and simulation of stochastic differential equations are explored, providing additional tools for model analysis and evaluation. This unified framework sits "at the intersection" of different mathematical subject areas, data science, statistics, and the natural sciences. The text throughout emphasizes data science workflows using the R statistical software program and the tidyverse constellation of packages. Only knowledge of calculus is needed; the text’s integrated framework is a stepping stone for further advanced study in mathematics or as a comprehensive introduction to modeling for quantitative natural scientists. The text will introduce you to: modeling with systems of differential equations and developing analytical, computational, and visual solution techniques. the R programming language, the tidyverse syntax, and developing data science workflows. qualitative techniques to analyze a system of differential equations. data assimilation techniques (simple linear regression, likelihood or cost functions, and Markov Chain, Monte Carlo Parameter Estimation) to parameterize models from data. simulating and evaluating outputs for stochastic differential equation models. An associated R package provides a framework for computation and visualization of results. It can be found here: https://cran.r-project.org/web/packages/demodelr/index.html.

An Introduction to the Statistical Theory of Classical Simple Dense Fluids covers certain aspects of the study of dense fluids, based on the analysis of the correlation effects between representative small groupings of molecules. The book starts by discussing empirical considerations including the physical characteristics of fluids; measured molecular spatial distribution; scattering by a continuous medium; the radial distribution function; the mean potential; and the molecular motion in liquids. The text describes the application of the theories to the description of dense fluids (i.e. interparticle force, classical particle trajectories, and the Liouville Theorem) and the deduction of expressions for the fluid thermodynamic functions. The theory of equilibrium short-range order by using the concept of closure approximation or total correlation; some numerical consequences of the equilibrium theory; and irreversibility are also looked into. The book further tackles the kinetic derivation of the Maxwell-Boltzmann (MB) equation; the statistical derivation of the MB equation; the movement to equilibrium; gas in a steady state; and viscosity and thermal conductivity. The text also discusses non-equilibrium liquids. Physicists, chemists, and engineers will find the book invaluable.

"This accessible volume presents both the mechanics of structural equation modeling (SEM) and specific SEM strategies and applications. The editor, along with an international group of contributors, and editorial advisory board are leading methodologists who have organized the book to move from simpler material to more statistically complex modeling approaches. Sections cover the foundations of SEM; statistical underpinnings, from assumptions to model modifications; steps in implementation, from data preparation through writing the SEM report; and basic and advanced applications, including new and emerging topics in SEM. Each chapter provides conceptually oriented descriptions, fully explicated analyses, and engaging examples that reveal modeling possibilities for use with readers' data. Many of the chapters also include access to data and syntax files at the companion website, allowing readers to try their hands at reproducing the authors' results"--