Here we present a nearly complete treatment of the Grand Universe of linear and weakly nonlinear regression models within the first 8 chapters. Our point of view is both an algebraic view as well as a stochastic one. For example, there is an equivalent lemma between a best, linear uniformly unbiased estimation (BLUUE) in a Gauss-Markov model and a least squares solution (LESS) in a system of linear equations. While BLUUE is a stochastic regression model, LESS is an algebraic solution. In the first six chapters we concentrate on underdetermined and overdeterimined linear systems as well as systems with a datum defect. We review estimators/algebraic solutions of type MINOLESS, BLIMBE, BLUMBE, BLUUE, BIQUE, BLE, BIQUE and Total Least Squares. The highlight is the simultaneous determination of the first moment and the second central moment of a probability distribution in an inhomogeneous multilinear estimation by the so called E-D correspondence as well as its Bayes design. In addition, we discuss continuous networks versus discrete networks, use of Grassmann-Pluecker coordinates, criterion matrices of type Taylor-Karman as well as FUZZY sets. Chapter seven is a speciality in the treatment of an overdetermined system of nonlinear equations on curved manifolds. The von Mises-Fisher distribution is characteristic for circular or (hyper) spherical data. Our last chapter eight is devoted to probabilistic regression, the special Gauss-Markov model with random effects leading to estimators of type BLIP and VIP including Bayesian estimation. A great part of the work is presented in four Appendices. Appendix A is a treatment, of tensor algebra, namely linear algebra, matrix algebra and multilinear algebra. Appendix B is devoted to sampling distributions and their use in terms of confidence intervals and confidence regions. Appendix C reviews the elementary notions of statistics, namely random events and stochastic processes. Appendix D introduces the basics of Groebner basis algebra, its careful definition, the Buchberger Algorithm, especially the C. F. Gauss combinatorial algorithm.
This book provides numerous examples of linear and nonlinear model applications. Here, we present a nearly complete treatment of the Grand Universe of linear and weakly nonlinear regression models within the first 8 chapters. Our point of view is both an algebraic view and a stochastic one. For example, there is an equivalent lemma between a best, linear uniformly unbiased estimation (BLUUE) in a Gauss–Markov model and a least squares solution (LESS) in a system of linear equations. While BLUUE is a stochastic regression model, LESS is an algebraic solution. In the first six chapters, we concentrate on underdetermined and overdetermined linear systems as well as systems with a datum defect. We review estimators/algebraic solutions of type MINOLESS, BLIMBE, BLUMBE, BLUUE, BIQUE, BLE, BIQUE, and total least squares. The highlight is the simultaneous determination of the first moment and the second central moment of a probability distribution in an inhomogeneous multilinear estimation by the so-called E-D correspondence as well as its Bayes design. In addition, we discuss continuous networks versus discrete networks, use of Grassmann–Plucker coordinates, criterion matrices of type Taylor–Karman as well as FUZZY sets. Chapter seven is a speciality in the treatment of an overjet. This second edition adds three new chapters: (1) Chapter on integer least squares that covers (i) model for positioning as a mixed integer linear model which includes integer parameters. (ii) The general integer least squares problem is formulated, and the optimality of the least squares solution is shown. (iii) The relation to the closest vector problem is considered, and the notion of reduced lattice basis is introduced. (iv) The famous LLL algorithm for generating a Lovasz reduced basis is explained. (2) Bayes methods that covers (i) general principle of Bayesian modeling. Explain the notion of prior distribution and posterior distribution. Choose the pragmatic approach for exploring the advantages of iterative Bayesian calculations and hierarchical modeling. (ii) Present the Bayes methods for linear models with normal distributed errors, including noninformative priors, conjugate priors, normal gamma distributions and (iii) short outview to modern application of Bayesian modeling. Useful in case of nonlinear models or linear models with no normal distribution: Monte Carlo (MC), Markov chain Monte Carlo (MCMC), approximative Bayesian computation (ABC) methods. (3) Error-in-variables models, which cover: (i) Introduce the error-in-variables (EIV) model, discuss the difference to least squares estimators (LSE), (ii) calculate the total least squares (TLS) estimator. Summarize the properties of TLS, (iii) explain the idea of simulation extrapolation (SIMEX) estimators, (iv) introduce the symmetrized SIMEX (SYMEX) estimator and its relation to TLS, and (v) short outview to nonlinear EIV models. The chapter on algebraic solution of nonlinear system of equations has also been updated in line with the new emerging field of hybrid numeric-symbolic solutions to systems of nonlinear equations, ermined system of nonlinear equations on curved manifolds. The von Mises–Fisher distribution is characteristic for circular or (hyper) spherical data. Our last chapter is devoted to probabilistic regression, the special Gauss–Markov model with random effects leading to estimators of type BLIP and VIP including Bayesian estimation. A great part of the work is presented in four appendices. Appendix A is a treatment, of tensor algebra, namely linear algebra, matrix algebra, and multilinear algebra. Appendix B is devoted to sampling distributions and their use in terms of confidence intervals and confidence regions. Appendix C reviews the elementary notions of statistics, namely random events and stochastic processes. Appendix D introduces the basics of Groebner basis algebra, its careful definition, the Buchberger algorithm, especially the C. F. Gauss combinatorial algorithm.
This is a second edition to the original published by Springer in 2006. The comprehensive volume takes a textbook approach systematically developing the field by starting from linear models and then moving up to generalized linear and non-linear mixed effects models. Since the first edition was published the field has grown considerably in terms of maturity and technicality. The second edition of the book therefore considerably expands with the addition of three new chapters relating to Bayesian models, Generalized linear and nonlinear mixed effects models, and Principles of simulation. In addition, many of the other chapters have been expanded and updated.
Using diverse real-world examples, this text examines what models used for data analysis mean in a specific research context. What assumptions underlie analyses, and how can you check them? Building on the successful 'Data Analysis and Graphics Using R,' 3rd edition (Cambridge, 2010), it expands upon topics including cluster analysis, exponential time series, matching, seasonality, and resampling approaches. An extended look at p-values leads to an exploration of replicability issues and of contexts where numerous p-values exist, including gene expression. Developing practical intuition, this book assists scientists in the analysis of their own data, and familiarizes students in statistical theory with practical data analysis. The worked examples and accompanying commentary teach readers to recognize when a method works and, more importantly, when it doesn't. Each chapter contains copious exercises. Selected solutions, notes, slides, and R code are available online, with extensive references pointing to detailed guides to R.
Reveals How HMMs Can Be Used as General-Purpose Time Series Models Implements all methods in R Hidden Markov Models for Time Series: An Introduction Using R applies hidden Markov models (HMMs) to a wide range of time series types, from continuous-valued, circular, and multivariate series to binary data, bounded and unbounded counts, and categorical observations. It also discusses how to employ the freely available computing environment R to carry out computations for parameter estimation, model selection and checking, decoding, and forecasting. Illustrates the methodology in action After presenting the simple Poisson HMM, the book covers estimation, forecasting, decoding, prediction, model selection, and Bayesian inference. Through examples and applications, the authors describe how to extend and generalize the basic model so it can be applied in a rich variety of situations. They also provide R code for some of the examples, enabling the use of the codes in similar applications. Effectively interpret data using HMMs This book illustrates the wonderful flexibility of HMMs as general-purpose models for time series data. It provides a broad understanding of the models and their uses.
A look at how wildlife professionals can modernize their approaches to habitat and population management with a fresh take on animal ecology. How can we maximize the probability that a species of wild animal will persist into the future? This audacious book proposes that advancing animal ecology—and conservation itself—demands that we reenvision our basic understanding of how animals interact with their environments and with each other. Synthesizing where we are and where we need to go with our studies of animals and their environs, Foundations for Advancing Animal Ecology asserts that studies of animal ecology should begin with a focus on the behaviors and characteristics of individual organisms. The book examines • the limitations of classic approaches to the study of animal ecology • how organisms organize into collections, such as breeding pairs, flocks, and herds • how the broader biotic and abiotic environment shapes animal populations, communities, and ecosystems • factors underlying the distribution and abundance of species through space and time • the links between habitat and population • why communication between researchers and managers is key • specific strategies for managing wild animal populations and habitats in an evolutionary and ecosystem context Throughout, the authors stress the importance of speaking a common and well-defined language. Avoiding vague and misleading terminology, they argue, will help ecologists translate science into meaningful and lasting actions in the environment. Taking the perspective of the organism of interest in developing concepts and applications, the authors always keep the potentially biased human perspective in focus. A major advancement in understanding the factors underlying wildlife-habitat relationships, Foundations for Advancing Animal Ecology will be an invaluable resource to professionals and practitioners in natural resource management in public and private sectors, including state and federal agencies, non-governmental organizations, and environmental consultants.
In a context of increased politicization led by state and federal policymakers, corporate reformers, and for-profit educational organizations, The Politics of Education Policy in an Era of Inequality explores a new vision for leading schools grounded in culturally relevant advocacy and social justice theories. This timely volume tackles the origins and implications of growing accountability for educational leaders and reconsiders the role that educational leaders should and can play in education policy and political processes. This book provides a critical perspective and analysis of today’s education policy landscape and leadership practice; explores the challenges and opportunities associated with teaching in and leading schools; and examines the structural, political, and cultural interactions among school principals, district leaders, and state and federal policy actors. An important resource for practicing and aspiring leaders, The Politics of Education Policy in an Era of Inequality shares a theoretical framework and strategies for building bridges between education researchers, practitioners, and policymakers.
Stress is an adaptive response that has developed throughout evolution, and is associated with multiple changes in the biochemistry, histology and physiology of an organism. As stress may induce or contribute to multiple neuropsychiatric disorders, the rigorous investigation of the neural substrates of stress has become a critical endeavour of contemporary biomedical science. This book explores the physiological responses that researchers have attempted to assess in stress-evoked behavioural changes, using both human and animal "experimental" models in clinical and non-clinical research. Though all aspects of the field have their challenges, animal experimental research of stress is a particularly difficult but meaningful task. This book provides succinct and relevant summaries of progress in the area of assessing stress response and the conditions contributing to it.
The clinical trial is “the most definitive tool for evaluation of the applicability of clinical research.” It represents “a key research activity with the potential to improve the quality of health care and control costs through careful comparison of alternative treatments” [1]. It has been called on many occasions, “the gold st- dard” against which all other clinical research is measured. Although many clinical trials are of high quality, a careful reader of the medical literature will notice that a large number have deficiencies in design, conduct, analysis, presentation, and/or interpretation of results. Improvements have occurred over the past few decades, but too many trials are still conducted without adequate attention to its fundamental principles. Certainly, numerous studies could have been upgraded if the authors had had a better understanding of the fundamentals. Since the publication of the first edition of this book, a large number of other texts on clinical trials have appeared, most of which are indicated here [2–21]. Several of them, however, discuss only specific issues involved in clinical trials. Additionally, many are no longer current. The purpose of this fourth edition is to update areas in which major progress has been made since the publication of the third edition. We have revised most chapters considerably and added one on ethical issues.
Discrete-valued time series are common in practice, but methods for their analysis are not well-known. In recent years, methods have been developed which are specifically designed for the analysis of discrete-valued time series. Hidden Markov and Other Models for Discrete-Valued Time Series introduces a new, versatile, and computationally tractable class of models, the "hidden Markov" models. It presents a detailed account of these models, then applies them to data from a wide range of diverse subject areas, including medicine, climatology, and geophysics. This book will be invaluable to researchers and postgraduate and senior undergraduate students in statistics. Researchers and applied statisticians who analyze time series data in medicine, animal behavior, hydrology, and sociology will also find this information useful.
This second, much enlarged edition by Lehmann and Casella of Lehmann's classic text on point estimation maintains the outlook and general style of the first edition. All of the topics are updated, while an entirely new chapter on Bayesian and hierarchical Bayesian approaches is provided, and there is much new material on simultaneous estimation. Each chapter concludes with a Notes section which contains suggestions for further study. This is a companion volume to the second edition of Lehmann's "Testing Statistical Hypotheses".
Sample surveys provide data used by researchers in a large range of disciplines to analyze important relationships using well-established and widely used likelihood methods. The methods used to select samples often result in the sample differing in important ways from the target population and standard application of likelihood methods can lead to biased and inefficient estimates. Maximum Likelihood Estimation for Sample Surveys presents an overview of likelihood methods for the analysis of sample survey data that account for the selection methods used, and includes all necessary background material on likelihood inference. It covers a range of data types, including multilevel data, and is illustrated by many worked examples using tractable and widely used models. It also discusses more advanced topics, such as combining data, non-response, and informative sampling. The book presents and develops a likelihood approach for fitting models to sample survey data. It explores and explains how the approach works in tractable though widely used models for which we can make considerable analytic progress. For less tractable models numerical methods are ultimately needed to compute the score and information functions and to compute the maximum likelihood estimates of the model parameters. For these models, the book shows what has to be done conceptually to develop analyses to the point that numerical methods can be applied. Designed for statisticians who are interested in the general theory of statistics, Maximum Likelihood Estimation for Sample Surveys is also aimed at statisticians focused on fitting models to sample survey data, as well as researchers who study relationships among variables and whose sources of data include surveys.
The Statistical Analysis of Multivariate Failure Time Data: A Marginal Modeling Approach provides an innovative look at methods for the analysis of correlated failure times. The focus is on the use of marginal single and marginal double failure hazard rate estimators for the extraction of regression information. For example, in a context of randomized trial or cohort studies, the results go beyond that obtained by analyzing each failure time outcome in a univariate fashion. The book is addressed to researchers, practitioners, and graduate students, and can be used as a reference or as a graduate course text. Much of the literature on the analysis of censored correlated failure time data uses frailty or copula models to allow for residual dependencies among failure times, given covariates. In contrast, this book provides a detailed account of recently developed methods for the simultaneous estimation of marginal single and dual outcome hazard rate regression parameters, with emphasis on multiplicative (Cox) models. Illustrations are provided of the utility of these methods using Women’s Health Initiative randomized controlled trial data of menopausal hormones and of a low-fat dietary pattern intervention. As byproducts, these methods provide flexible semiparametric estimators of pairwise bivariate survivor functions at specified covariate histories, as well as semiparametric estimators of cross ratio and concordance functions given covariates. The presentation also describes how these innovative methods may extend to handle issues of dependent censorship, missing and mismeasured covariates, and joint modeling of failure times and covariates, setting the stage for additional theoretical and applied developments. This book extends and continues the style of the classic Statistical Analysis of Failure Time Data by Kalbfleisch and Prentice. Ross L. Prentice is Professor of Biostatistics at the Fred Hutchinson Cancer Research Center and University of Washington in Seattle, Washington. He is the recipient of COPSS Presidents and Fisher awards, the AACR Epidemiology/Prevention and Team Science awards, and is a member of the National Academy of Medicine. Shanshan Zhao is a Principal Investigator at the National Institute of Environmental Health Sciences in Research Triangle Park, North Carolina.
Summary: "As memristors are not yet on the market, the development of memristor emulators and memristor based circuits is very important for real and practical engineering applications. The objectives of this book are to review the basic concepts of the memristor, describe state-of-the-art memristor based circuits and to stimulate further research and development in this area."--Preface.
This book traces changing perceptions of Egypt's monastic landscape through an analysis of archaeological and documentary evidence from late antiquity.
Nonlinear dynamics of complex processes is an active research field with large numbers of publications in basic research, and broad applications from diverse fields of science. Nonlinear dynamics as manifested by deterministic and stochastic evolution models of complex behavior has entered statistical physics, physical chemistry, biophysics, geophysics, astrophysics, theoretical ecology, semiconductor physics and -optics, etc. This field of research has induced a new terminology in science connected with new questions, problems, solutions and methods. New scenarios have emerged for spatio-temporal structures in dynamical systems far from equilibrium. Their analysis and possible control are intriguing and challenging aspects of the current research.The duality of fundamental and applied research is a focal point of its main attractivity and fascination. Basic topics and foundations are always linked to concrete and precise examples. Models and measurements of complex nonlinear processes evoke and provoke new fundamental questions that diversify and broaden the mathematical concepts and tools. In return, new mathematical approaches to modeling and analysis enlarge the scope and efficiency of applied research.
Statistical Concepts—A Second Course presents the last 10 chapters from An Introduction to Statistical Concepts, Fourth Edition. Designed for second and upper-level statistics courses, this book highlights how statistics work and how best to utilize them to aid students in the analysis of their own data and the interpretation of research results. In this new edition, Hahs-Vaughn and Lomax discuss sensitivity, specificity, false positive and false negative errors. Coverage of effect sizes has been expanded upon and more organizational features (to summarize key concepts) have been included. A final chapter on mediation and moderation has been added for a more complete presentation of regression models. In addition to instructions and screen shots for using SPSS, new to this edition is annotated script for using R. This book acts as a clear and accessible instructional tool to help readers fully understand statistical concepts and how to apply them to data. It is an invaluable resource for students undertaking a course in statistics in any number of social science and behavioral science disciplines.
Refugees from Poland first came to Salem in the 1880s when the former maritime port became a leading industrial center. These immigrants often arrived with little more than the clothes on their backs and worked some of the most dangerous factory jobs. However, despite limited knowledge of the English language and American customs, they persevered to improve their lives and the lives of their children. The Polish Community of Salem chronicles the social, economic, and cultural transitions that took place as Polish immigrants started life anew in Salem, created a vibrant community, gained US citizenship, and assimilated into American society.
First published in 1980, Murano and Bick provide well rounded accounts into the effects of Hemostasis and Thrombosis as well as a guide to the general physiology, associated disorders and therapeutic techniques used to address them. This book is ideal for students and practicioners of Hematology as well as those with a general interest in medicine.
This widely acclaimed book is a complete, authoritative reference on nutrition and its role in contemporary medicine, dietetics, nursing, public health, and public policy. Distinguished international experts provide in-depth information on historical landmarks in nutrition, specific dietary components, nutrition in integrated biologic systems, nutritional assessment through the life cycle, nutrition in various clinical disorders, and public health and policy issues. Modern Nutrition in Health and Disease, Eleventh Edition, offers coverage of nutrition's role in disease prevention, international nutrition issues, public health concerns, the role of obesity in a variety of chronic illnesses, genetics as it applies to nutrition, and areas of major scientific progress relating nutrition to disease.
School, Family, and Community Partnerships: Preparing Educators and Improving Schools, 3rd Edition prepares future teachers and administrators to conduct effective and equitable programs of family and community engagement that contribute to student success in school. Renowned authors Joyce L. Epstein and Steven B. Sheldon present the theories, research, policies, and practices that have been shown to improve the design and conduct of partnership programs in diverse communities and at all grade levels. Chapters include a historic overview of early research, recent studies with advanced methods, and many examples of research-based approaches for district leadership and school improvement. All chapters include discussion questions and classroom assignments that professors may use to provoke thinking and help future educators understand that family and community engagement is part of their professional work. New in this 3rd Edition: Updated, streamlined readings make it easy for students to explore early, influential studies that framed the field and recent studies of multilevel effects of leadership for partnerships. Comments, discussion topics, and classroom activities challenge students to think deeply about many aspects and issues of school, family, and community partnerships. Interview assignments enable students to hear the voices and views on partnerships of practicing educators, parents, students, and others in the community. Readings and activities across chapters help colleges and universities meet new standards of the Council for the Accreditation of Educator Preparation (CAEP) for graduates to "demonstrate their ability to effectively work with diverse P-12 students and their families." After decades of research and exemplary practice that confirm that family and community engagement is an essential component of good school organization, most new teachers and administrators still are unprepared to partner with all families to support student learning and development. This book will help professors in Schools, Colleges, and Departments of Education (SCDE) prepare their graduates to understand, organize, and continually improve partnership programs in all schools, with all families, and for all students.
Recipient of the 2017 Most Promising New Textbook Award from the Textbook & Academic Authors Association (TAA) Chronologically organized, Lifespan Development: Lives in Context offers a unique perspective on the field by focusing on the importance of context—examining how the places, sociocultural environments, and ways in which we are raised influence who we become and how we grow and change. Author Tara L. Kuther integrates cutting-edge and classic research throughout the text to present a unified story of developmental science and its applications to everyday life. Robust pedagogy, student-friendly writing, and an inviting design enhance this exciting and inclusive exploration of the ways in which context informs our understanding of the lifespan.
This second edition of Applied Multivariate Statistical Concepts covers the classic and cutting-edge multivariate techniques used in today’s research. Through clear writing and engaging pedagogy and examples using real data, Hahs-Vaughn walks students through the most used methods to learn why and how to apply each technique. A conceptual approach with a higher than usual text-to-formula ratio helps readers master key concepts so they can implement and interpret results generated by today’s sophisticated software. Additional features include examples using real data from the social sciences; templates for writing research questions and results that provide manuscript-ready models; step-by-step instructions on using R and SPSS statistical software with screenshots and annotated output; clear coverage of assumptions, including how to test them and the effects of their violation; and conceptual, computational, and interpretative example problems that mirror the real-world problems students encounter in their studies and careers. This edition features expanded coverage of topics, such as propensity score analysis, path analysis and confirmatory factor analysis, and centering, moderation effects, and power as related to multilevel modelling. New topics are introduced, such as addressing missing data and latent class analysis, while each chapter features an introduction to using R statistical software. This textbook is ideal for courses on multivariate statistics/analysis/design, advanced statistics, and quantitative techniques, as well as for graduate students broadly in social sciences, education, and behavioral sciences. It also appeals to researchers with no training in multivariate methods.
The behaviour of many Poles towards the Jewish population during the Nazi occupation of Poland has always been a controversial issue. Although the Poles are supposed not to have collaborated with the invaders, there is evidence to show that in respect of the Jewish population, the behaviour of many Poles, including members of the underground, was far from exemplary. Poland is also the only European country where Jews were being murdered after the end of the war and where strong anti-Semitic tendencies are still present. This book analyses this question in an historical context and attempts to offer an explanation for the phenomenon of Polish anti-Semitism during and after the end of the war. The work is based on recently uncovered documents as well as on personal accounts of witnesses to the events during the war.
The book studies the trends that led to the worst financial crisis since the Great Depression, as well as the unfolding of the crisis, in order to provide policy recommendations to improve financial stability. The book starts with changes in monetary policy and income distribution from the 1970s. These changes profoundly modified the foundations of economic growth in the US by destroying the commitment banking model and by decreasing the earning power of households whose consumption has been at the core of the growth process. The main themes of the book are the changes in the financial structure and income distribution, the collapse of the Ponzi process in 2007, and actual and prospective policy responses. The objective is to show that Minsky’s approach can be used to understand the making and unfolding of the crisis and to draw some policy implications to improve financial stability.
This book provides an introduction to the theory, method, and practice of State-Trace Analysis (STA), and includes a detailed tutorial on the statistical analysis of state-trace designs. The book offers instructions on how to perform state-trace analysis using the authors' own publicly-available software in both Matlab and R. The book begins by discussing the general framework for thinking about the relationships between independent variables, latent variables, and dependent variables. Subsequent chapters provide a software package that can be used to fit state-trace models as well as additional designs and examples. The book concludes with a discussion on potential extensions of STA and additional aspects of its application. State-Trace Analysis will be of interest to researchers and graduate students working in experimental, applied, and cognitive psychology.
This book provides a penetrating and comprehensive description of energy selected reactions from a theoretical as well as experimental view. Three major aspects of unimolecular reactions involving the preparation of the reactants in selected energy states, the rate of dissociation of the activated molecule, and the partitioning of the excess energy among the final products, are fully discussed with the aid of 175 illustrations and over 1,000 references, most from the recent literature. Examples of both neutral and ionic reactions are presented. Many of the difficult topics are discussed at several levels of sophistication to allow access by novices as well as experts. Among the topics covered for the first time in monograph form is a discussion of highly excited vibrational/rotational states and intramolecular vibrational energy redistribution. Problems associated with the application of RRKM theory are discussed with the aid of experimental examples. Detailed comparisons are also made between different statistical models of unimolecular decomposition. Both quantum and classical models not based on statistical assumptions are described. Finally, a chapter devoted to the theory of product energy distribution includes the application of phase space theory to the dissociation of small and large clusters. The work will be welcomed as a valuable resource by practicing researchers and graduate students in physical chemistry, and those involved in the study of chemical reaction dynamics.
From its beginnings at the turn of the 20th century to its pervasive presence in 21st-century America, basketball has grown into an undeniably important sport. The 575 entries in this biographical dictionary present concise narratives on the lives and careers on the most important names in basketball history. Entries include both classic players such as Kareem Abdul-Jabbar and Bob Cousy as well as more recently established and up-and-coming stars such as Carmelo Anthony, Kevin Garnett, and LeBron James. Entries for coaches such as the Boston Celtics' Red Auerbach and Mike Krzyzewski from Duke University present the figures who have shaped the game from courtside, while the inclusion of female players and coaches such as Lisa Leslie, Diana Taurasi, and Pat Summitt show that basketball is not just a sport for men. From its beginnings at the turn of the 20th century to its pervasive presence in 21st-century America, basketball has grown into an undeniably important sport. The 575 entries in this biographical dictionary present concise narratives on the lives and careers on the most important names in basketball history. Entries include both classic players such as Kareem Abdul-Jabbar and Bob Cousy as well as more recently established and up-and-coming stars such as Carmelo Anthony, Kevin Garnett, and LeBron James. Entries for coaches such as the Boston Celtics' Red Auerbach and Mike Krzyzewski from Duke University present the figures who have shaped the game from courtside, while the inclusion of female players and coaches such as Lisa Leslie, Diana Taurasi, and Pat Summitt show that basketball is not just a sport for men. This volume is an ideal reference for students seeking easily accessed information on the greats of the game.
Exact sampling, specifically coupling from the past (CFTP), allows users to sample exactly from the stationary distribution of a Markov chain. During its nearly 20 years of existence, exact sampling has evolved into perfect simulation, which enables high-dimensional simulation from interacting distributions.Perfect Simulation illustrates the applic
The First Book Dedicated to This Class of Longitudinal Models Although antedependence models are particularly useful for modeling longitudinal data that exhibit serial correlation, few books adequately cover these models. By gathering results scattered throughout the literature, Antedependence Models for Longitudinal Data offers a convenient, systematic way to learn about antedependence models. Illustrated with numerous examples, the book also covers some important statistical inference procedures associated with these models. After describing unstructured and structured antedependence models and their properties, the authors discuss informal model identification via simple summary statistics and graphical methods. They then present formal likelihood-based procedures for normal antedependence models, including maximum likelihood and residual maximum likelihood estimation of parameters as well as likelihood ratio tests and penalized likelihood model selection criteria for the model’s covariance structure and mean structure. The authors also compare the performance of antedependence models to other models commonly used for longitudinal data. With this book, readers no longer have to search across widely scattered journal articles on the subject. The book provides a thorough treatment of the properties and statistical inference procedures of various antedependence models.
Many econometric models contain unknown functions as well as finite- dimensional parameters. Examples of such unknown functions are the distribution function of an unobserved random variable or a transformation of an observed variable. Econometric methods for estimating population parameters in the presence of unknown functions are called "semiparametric." During the past 15 years, much research has been carried out on semiparametric econometric models that are relevant to empirical economics. This book synthesizes the results that have been achieved for five important classes of models. The book is aimed at graduate students in econometrics and statistics as well as professionals who are not experts in semiparametic methods. The usefulness of the methods will be illustrated with applications that use real data.
Now in a fully revised Fourth Edition, Modern Epidemiology remains the gold standard text in this complex and evolving field. This edition continues to provide comprehensive coverage of the principles and methods for the design, analysis, and interpretation of epidemiologic research. Featuring a new format allowing space for margin notes, this edition • Reflects both the conceptual development of this evolving science and the increasing role that epidemiology plays in improving public health and medicine. • Features new coverage of methods such as agent-based modeling, quasi-experimental designs, mediation analysis, and causal modeling. • Updates coverage of methods such as concepts of interaction, bias analysis, and time-varying designs and analysis. • Continues to cover the full breadth of epidemiologic methods and concepts, including epidemiologic measures of occurrence and effect, study designs, validity, precision, statistical interference, field methods, surveillance, ecologic designs, and use of secondary data sources. • Includes data analysis topics such as Bayesian analysis, probabilistic bias analysis, time-to-event analysis, and an extensive overview of modern regression methods including logistic and survival regression, splines, longitudinal and cluster-correlated/hierarchical data analysis, propensity scores and other scoring methods, and marginal structural models. • Summarizes the history, specialized aspects, and future directions of topical areas, including among others social epidemiology, infectious disease epidemiology, genetic and molecular epidemiology, psychiatric epidemiology, injury and violence epidemiology, and pharmacoepidemiology.
This widely acclaimed book is a complete, authoritative reference on nutrition and its role in contemporary medicine, dietetics, nursing, public health, and public policy. Distinguished international experts provide in-depth information on historical landmarks in nutrition, specific dietary components, nutrition in integrated biologic systems, nutritional assessment through the life cycle, nutrition in various clinical disorders, and public health and policy issues. Modern Nutrition in Health and Disease, Eleventh Edition, offers coverage of nutrition's role in disease prevention, international nutrition issues, public health concerns, the role of obesity in a variety of chronic illnesses, genetics as it applies to nutrition, and areas of major scientific progress relating nutrition to disease.
This massively updated and expanded fifth edition is the most complete, authoritative engineering treatment of the dehydration and gas purification processes used in industry today. Of great value to design and operations engineers, it gives practical process and equipment design descriptions, basic data, plant performance results, and other detailed information on gas purification processes and hardware. This latest edition incorporates all significant advances in the field since 1985.You will find major new chapters on the rapidly expanding technologies of nitrogen oxide control, with discussions of regulatory requirements and available processes; absorption in physical solvents, covering single component and mixed solvent systems; and membrane permeation, with emphasis on the gas purification applications of membrane units. In addition, new sections cover areas of strong current interest, particularly liquid hydrocarbon treating, Claus plant tail gas treating, thermal oxidation of volatile organic compounds, and sulfur scavenging processes.This volume brings you expanded coverage of alkanolamines for hydrogen sulfide and carbon dioxide removal, the removal and use of ammonia in gas purification, the use of alkaline salt solutions for acid gas removal, and the use of water to absorb gas impurities. The basic technologies and all significant advances in the following areas are thoroughly described: sulfur dioxide removal and recovery processes, processes for converting hydrogen sulfide to sulfur, liquid phase oxidation processes for hydrogen sulfide removal, the absorption of water vapor by dehydrating solutions, gas dehydration and purification by adsorption, and the catalytic and thermal conversion of gas impurities.
This thoroughly revised and updated edition of a classic reference work is the definitive guide to the identification of the parasites of freshwater fishes of North America. The book provides information on public health concerns about fish parasites, the methods used to examine fish for parasites, and those parasites found only in very selective organs or tissues. It lists the known species of each genus, along with reference citations that enable readers to find literature pertinent to species identification, life cycles, and in some cases, control.
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