Complex Data Modeling and Computationally Intensive Statistical Methods

Written by a group of international experts in their field, this book is a review of Lagrangian observation, analysis and assimilation methods in physical and biological oceanography. This multidisciplinary text presents new results on nonlinear analysis of Lagrangian dynamics, the prediction of particle trajectories, and Lagrangian stochastic models. It includes historical information, up-to-date developments, and speculation on future developments in Lagrangian-based observations, analysis, and modeling of physical and biological systems. Containing contributions from experimentalists, theoreticians, and modelers in the fields of physical oceanography, marine biology, mathematics, and meteorology, this book will be of great interest to researchers and graduate students looking for both practical applications and information on the theory of transport and dispersion in physical systems, biological modeling, and data assimilation.

Flood inundation models enable us to make hazard predictions for floodplains, mitigating increasing flood fatalities and losses. This book provides an understanding of hydraulic modelling and floodplain dynamics, with a key focus on state-of-the-art remote sensing data, and methods to estimate and communicate uncertainty. Academic researchers in the fields of hydrology, climate change, environmental science and natural hazards, and professionals and policy-makers working in flood risk mitigation, hydraulic engineering and remote sensing will find this an invaluable resource. This volume is the third in a collection of four books on flood disaster management theory and practice within the context of anthropogenic climate change. The others are: Floods in a Changing Climate: Extreme Precipitation by Ramesh Teegavarapu, Floods in a Changing Climate: Hydrological Modeling by P. P. Mujumdar and D. Nagesh Kumar and Floods in a Changing Climate: Risk Management by Slodoban Simonović.

The scientific advances that underpin economic growth and human health would not be possible without research investments. Yet demonstrating the impact of research programs is a challenge, especially in areas that span disciplines, industrial sectors, and encompass both public and private sector activity. All areas of research are under pressure to demonstrate benefits from federal funding of research. This exciting and innovative study demonstrates new methods and tools to trace the impact of federal research funding on the structure of research, and the subsequent economic activities of funded researchers. The case study is food safety research, which is critical to avoiding outbreaks of disease. The authors make use of an extraordinary new data infrastructure and apply new techniques in text analysis. Focusing on the impact of US federal food safety research, this book develops vital data-intensive methodologies that have a real world application to many other scientific fields.

East Asia is richer, more integrated and more stable than ever before, whilst East Asian defense spending is now roughly half of what it was in 1990 and shows no sign of increasing. There is no evidence of any Asian arms race. All countries in the region are seeking diplomatic, not military solutions with each other. Yet this East Asia reality still runs counter to a largely Western narrative that views China's rise as a threat and the region as increasingly unstable. In this important book, David C. Kang argues that American grand strategy should emphasize diplomatic and economic relations with the region, rather than military-first policies. Using longitudinal and comparative data, statistical analysis, and intensive research in selected East Asian countries, he suggests that East Asia is in sync with the American desire to share burdens and that the region may in fact be more stable than popularly believed.

Multilayer networks, in particular multilayer social networks, where users belong to and interact on different networks at the same time, are an active research area in social network analysis, computer science, and physics. These networks have traditionally been studied within these separate research communities, leading to the development of several independent models and methods to deal with the same set of problems. This book unifies and consolidates existing practical and theoretical knowledge on multilayer networks including data collection and analysis, modeling, and mining of multilayer social network systems, the evolution of interconnected social networks, and dynamic processes such as information spreading. A single real dataset is used to illustrate the concepts presented throughout the book, demonstrating both the practical utility and the potential shortcomings of the various methods. Researchers from all areas of network analysis will learn new aspects and future directi

The advent of supercomputers has brought computational fluid dynamics (CFD) to the forefront as a tool to analyze increasingly complex simulation scenarios in many fields. Computational aerodynamics problems are also increasingly moving towards being coupled, multi-physics and multi-scale with complex, moving geometries. The latter presents severe geometry handling and meshing challenges. Simulations also frequently use formal design optimization processes. This book explains the evolution of CFD and provides a comprehensive overview of the plethora of tools and methods available for solving complex scenarios while exploring the future directions and possible outcomes. Using numerous examples, illustrations and computational methods the author discusses turbulence modeling, pre and post processing, coupled solutions, the importance of design optimization, multiphysics problems, reduced order models, and large scale computations and the future of CFD. Advanced Computational Fluid and Ae

Storytelling is a fundamental mode of everyday interaction. This book is based upon the Narrative Corpus (NC), a specialized corpus of naturally occurring narratives, and provides new paths for its study. Christoph Rühlemann uses the NC's narrative-specific annotation and XPath and XQuery, query languages that allow the retrieval of complex data structures, to facilitate large-scale quantitative investigations into how narrators and recipients collaborate in storytelling. Empirical analyses are validated using R, a programming language and environment for statistical computing and graphics. Using this unique data and methodological base, Rühlemann reveals new insights, including the discovery of turntaking patterns specific to narrative, the first investigation of textual colligation in spoken data, the unearthing of how speech reports, as discourse units, form striking patterns at utterance level, and the identification of the story climax as the sequential context in which recipient

This book addresses the problems of modeling, prediction, classification, data understanding and processing in non-stationary and unpredictable environments. It presents major and well-known methods a

Coercive diplomacy - the use of threats and assurances to alter another state's behavior - is indispensable to international relations. Most scholarship has focused on whether and when states are able to use coercive methods to achieve their desired results. However, employing game-theoretic tools, statistical modeling, and detailed case study analysis, Power Plays builds and tests a theory that explains how states develop strategies of coercive diplomacy, how their targets shield themselves from these efforts, and the implications for interstate relations. Focusing on the World Trade Organization, Power Plays argues that coercive diplomacy often precludes cooperation due to fears of exploitation, but that international institutions can solve these problems by convincing states to eschew certain tools for coercive purposes.

Based on a starter course for beginning graduate students, Core Statistics provides concise coverage of the fundamentals of inference for parametric statistical models, including both theory and practical numerical computation. The book considers both frequentist maximum likelihood and Bayesian stochastic simulation while focusing on general methods applicable to a wide range of models and emphasizing the common questions addressed by the two approaches. This compact package serves as a lively introduction to the theory and tools that a beginning graduate student needs in order to make the transition to serious statistical analysis: inference; modeling; computation, including some numerics; and the R language. Aimed also at any quantitative scientist who uses statistical methods, this book will deepen readers' understanding of why and when methods work and explain how to develop suitable methods for non-standard situations, such as in ecology, big data and genomics.

"Traditional, low-dimensional, small scale data have been successfully dealt with using conventional software engineering and classical statistical methods, such as discriminant analysis, neural netwo

From the Ballot to the Blackboard provides the first comprehensive account of the political economy of education spending across the developed and developing world. The book demonstrates how political forces like democracy and political partisanship and economic factors like globalization deeply impact the choices made by voters, parties, and leaders in financing education. The argument is developed through three stories that track the historical development of education: first, its original expansion from the elite to the masses; second, the partisan politics of education in industrialized states; and third, the politics of higher education. The book uses a variety of complementary methods to demonstrate the importance of redistributive political motivations in explaining education policy, including formal modeling, statistical analysis of survey data and both sub-national and cross-national data, and historical case analyses of countries including the Philippines, India, Malaysia, En

Complexity science is the study of systems with many interdependent components. Such systems - and the self-organization and emergent phenomena they manifest - lie at the heart of many challenges of global importance. This book is a coherent introduction to the mathematical methods used to understand complexity, with plenty of examples and real-world applications. It starts with the crucial concepts of self-organization and emergence, then tackles complexity in dynamical systems using differential equations and chaos theory. Several classes of models of interacting particle systems are studied with techniques from stochastic analysis, followed by a treatment of the statistical mechanics of complex systems. Further topics include numerical analysis of PDEs, and applications of stochastic methods in economics and finance. The book concludes with introductions to space-time phases and selfish routing. The exposition is suitable for researchers, practitioners and students in complexity sci

The Jackknife and bootstrap are the most popular data-resampling methods used in statistical analysis. This book provides a systematic introduction to the theory of the jackknife, bootstrap and other

This unique book is the first comprehensive guide to the discovery, analysis, and evaluation of natural experiments - an increasingly popular methodology in the social sciences. Thad Dunning provides an introduction to key issues in causal inference, including model specification, and emphasizes the importance of strong research design over complex statistical analysis. Surveying many examples of standard natural experiments, regression-discontinuity designs, and instrumental-variables designs, Dunning highlights both the strengths and potential weaknesses of these methods, aiding researchers in better harnessing the promise of natural experiments while avoiding the pitfalls. Dunning also demonstrates the contribution of qualitative methods to natural experiments and proposes new ways to integrate qualitative and quantitative techniques. Chapters complete with exercises and appendices covering specialized topics such as cluster-randomized natural experiments, make this an ideal teachin

In this book Craig, Kinney and their collaborators confront the main unsolved mysteries in Shakespeare's canon through computer analysis of Shakespeare's and other writers' styles. In some cases their analysis confirms the current scholarly consensus, bringing long-standing questions to something like a final resolution. In other areas the book provides more surprising conclusions: that Shakespeare wrote the 1602 additions to The Spanish Tragedy, for example, and that Marlowe along with Shakespeare was a collaborator on Henry VI, Parts 1 and 2. The methods used are more wholeheartedly statistical, and computationally more intensive, than any that have yet been applied to Shakespeare studies. The book also reveals how word patterns help create a characteristic personal style. In tackling traditional problems with the aid of the processing power of the computer, harnessed through computer science, and drawing upon large amounts of data, the book is an exemplar of the new domain of digita

The small sample properties of estimators and tests are frequently too complex to be useful or are unknown. Much econometric theory is therefore developed for very large or asymptotic samples where it is assumed that the behaviour of estimators and tests will adequately represent their properties in small samples. Refined asymptotic methods adopt an intermediate position by providing improved approximations to small sample behaviour using asymptotic expansions. Dedicated to the memory of Michael Magdalinos, whose work is a major contribution to this area, this book contains chapters directly concerned with refined asymptotic methods. In addition, there are chapters focusing on new asymptotic results; the exploration through simulation of the small sample behaviour of estimators and tests in panel data models; and improvements in methodology. With contributions from leading econometricians, this collection will be essential reading for researchers and graduate students concerned with th

To provide useful and meaningful information, long-term ecological programs need to implement solid and efficient statistical approaches for collecting and analyzing data. This volume provides rigorous guidance on quantitative issues in monitoring, with contributions from world experts in the field. These experts have extensive experience in teaching fundamental and advanced ideas and methods to natural resource managers, scientists and students. The chapters present a range of tools and approaches, including detailed coverage of variance component estimation and quantitative selection among alternative designs; spatially balanced sampling; sampling strategies integrating design- and model-based approaches; and advanced analytical approaches such as hierarchical and structural equation modelling. Making these tools more accessible to ecologists and other monitoring practitioners across numerous disciplines, this is a valuable resource for any professional whose work deals with ecologic

The problems of making inferences about the natural world from noisy observations and imperfect theories occur in almost all scientific disciplines. This 2006 book addresses these problems using examples taken from geophysical fluid dynamics. It focuses on discrete formulations, both static and time-varying, known variously as inverse, state estimation or data assimilation problems. Starting with fundamental algebraic and statistical ideas, the book guides the reader through a range of inference tools including the singular value decomposition, Gauss-Markov and minimum variance estimates, Kalman filters and related smoothers, and adjoint (Lagrange multiplier) methods. The final chapters discuss a variety of practical applications to geophysical flow problems. Discrete Inverse and State Estimation Problems is an ideal introduction to the topic for graduate students and researchers in oceanography, meteorology, climate dynamics, and geophysical fluid dynamics. It is also accessible to a

Filtering and system identification are powerful techniques for building models of complex systems. This 2007 book discusses the design of reliable numerical methods to retrieve missing information in models derived using these techniques. Emphasis is on the least squares approach as applied to the linear state-space model, and problems of increasing complexity are analyzed and solved within this framework, starting with the Kalman filter and concluding with the estimation of a full model, noise statistics and state estimator directly from the data. Key background topics, including linear matrix algebra and linear system theory, are covered, followed by different estimation and identification methods in the state-space model. With end-of-chapter exercises, MATLAB simulations and numerous illustrations, this book will appeal to graduate students and researchers in electrical, mechanical and aerospace engineering. It is also useful for practitioners. Additional resources for this title,

Modern knowledge discovery methods enable users to discover complex patterns of various types in large information repositories. However, the underlying assumption has always been that the data to whi

Astronomy needs statistical methods to interpret data, but statistics is a many-faceted subject that is difficult for non-specialists to access. This handbook helps astronomers analyze the complex data and models of modern astronomy. This second edition has been revised to feature many more examples using Monte Carlo simulations, and now also includes Bayesian inference, Bayes factors and Markov chain Monte Carlo integration. Chapters cover basic probability, correlation analysis, hypothesis testing, Bayesian modelling, time series analysis, luminosity functions and clustering. Exercises at the end of each chapter guide readers through the techniques and tests necessary for most observational investigations. The data tables, solutions to problems, and other resources are available online at www.cambridge.org/9780521732499. Bringing together the most relevant statistical and probabilistic techniques for use in observational astronomy, this handbook is a practical manual for advanced und

The interdisciplinary nature of bioinformatics presents a research challenge in integrating concepts, methods, software and multiplatform data. Although there have been rapid developments in new technology and an inundation of statistical methods for addressing other types of high-throughput data, such as proteomic profiles that arise from mass spectrometry experiments. This book discusses the development and application of Bayesian methods in the analysis of high-throughput bioinformatics data that arise from medical, in particular, cancer research, as well as molecular and structural biology. The Bayesian approach has the advantage that evidence can be easily and flexibly incorporated into statistical methods. A basic overview of the biological and technical principles behind multi-platform high-throughput experimentation is followed by expert reviews of Bayesian methodology, tools and software for single group inference, group comparisons, classification and clustering, motif discov

An understanding of statistics and experimental design is essential for life science studies, but many students lack a mathematical background and some even dread taking an introductory statistics course. Using a refreshingly clear and encouraging reader-friendly approach, this book helps students understand how to choose, carry out, interpret and report the results of complex statistical analyses, critically evaluate the design of experiments and proceed to more advanced material. Taking a straightforward conceptual approach, it is specifically designed to foster understanding, demystify difficult concepts and encourage the unsure. Even complex topics are explained clearly, using a pictorial approach with a minimum of formulae and terminology. Examples of tests included throughout are kept simple by using small data sets. In addition, end-of-chapter exercises, new to this edition, allow self-testing. Handy diagnostic tables help students choose the right test for their work and remain

Applied statistics is more than data analysis, but it is easy to lose sight of the big picture. David Cox and Christl Donnelly distil decades of scientific experience into usable principles for the successful application of statistics, showing how good statistical strategy shapes every stage of an investigation. As you advance from research or policy question, to study design, through modelling and interpretation, and finally to meaningful conclusions, this book will be a valuable guide. Over a hundred illustrations from a wide variety of real applications make the conceptual points concrete, illuminating your path and deepening your understanding. This book is essential reading for anyone who makes extensive use of statistical methods in their work.

Econophysics is an emerging interdisciplinary field that takes advantage of the concepts and methods of statistical physics to analyse economic phenomena. This book expands the explanatory scope of econophysics to the real economy by using methods from statistical physics to analyse the success and failure of companies. Using large data sets of companies and income-earners in Japan and Europe, a distinguished team of researchers show how these methods allow us to analyse companies, from huge corporations to small firms, as heterogeneous agents interacting at multiple layers of complex networks. They then show how successful this approach is in explaining a wide range of recent findings relating to the dynamics of companies. With mathematics kept to a minimum, the book is not only a lively introduction to the field of econophysics but also provides fresh insights into company behaviour.

The advent of fiber optic transmission systems and wavelength division multiplexing (WDM) have led to a dramatic increase in the usable bandwidth of single fiber systems. This book provides detailed coverage of survivability (dealing with the risk of losing large volumes of traffic data due to a failure of a node or a single fiber span) and traffic grooming (managing the increased complexity of smaller user requests over high capacity data pipes), both of which are key issues in modern optical networks. A framework is developed to deal with these problems in wide-area networks, where the topology used to service various high-bandwidth (but still small in relation to the capacity of the fiber) systems evolves toward making use of a general mesh. Effective solutions, exploiting complex optimization techniques, and heuristic methods are presented to keep network problems tractable. Newer networking technologies and efficient design methodologies are also described.

Attempts by ecologists to establish models for predicting the growth of a population or the fluctuations of a natural resource can be confounded by environmental heterogeneity. Spatio-Temporal Heterogeneity explores a range of available statistical methods to help ecologists in the attempt to unravel complexities, demonstrating how to place these changes into an understandable statistical framework. It addresses several key questions, including how to interpret the parameters of statistical models in relation to the biological and environmental realities, how to design a study to collect the best sample data and how to avoid pitfalls in modelling, design, statistical assessment and interpretation. Dutilleul uses a variety of examples to facilitate understanding, from plant ecology, earth and atmospheric sciences, animal biology, forestry and limnology. The accompanying CD-ROM contains MATLAB and SAS codes to aid analyses.

The best alternative to chemical control is often seen as being biological control - the introduction of natural enemies in areas where foreign pests become abundant. However, biological control alone is not always sufficient to maintain pest populations under a tolerable level; and it is necessary to learn how to make it compatible with other methods of control, and in particular with chemicals, in integrated control programs. Data on these important aspects was collected and elaborated over a period of seven to eight years and synthesized in this volume, which was originally published in 1976. The data relates to five groups of species recognized as main agricultural pests over vast areas of developing countries. It shows how complex the interrelationships between microorganisms are how much research effort has to be invested in such disciplines as systematics, physiology, ethnology and ecology for their understanding.

This concise, self-contained and cohesive book focuses on commonly used and recently developed methods for designing and analyzing high-throughput screening (HTS) experiments from a statistically sound basis. Combining ideas from biology, computing and statistics, the author explains experimental designs and analytic methods that are amenable to rigorous analysis and interpretation of RNAi HTS experiments. The opening chapters are carefully presented to be accessible both to biologists with training only in basic statistics and to computational scientists and statisticians with basic biological knowledge. Biologists will see how new experiment designs and rudimentary data-handling strategies for RNAi HTS experiments can improve their results, whereas analysts will learn how to apply recently developed statistical methods to interpret HTS experiments.

Originally published in 1973, this book deals with the development of financial institutions, changes in methods of finance and the role of monetary control in socialist economic management. The structure and activity of financial institutions were deliberately adapted to changes in policy planning techniques used in socialist economies. Dr Podolski provides a systematic description of this process of adaptation, based mainly on the example of the Polish economy, on which he has gathered a large amount of statistical data. Throughout the book international comparisons are made. Soviet experience is particularly relevant, but attempts are also made at comparisons with Western economies. Dr Podolski's study sheds a great deal of light on the role of the credit system in a centralised economy and will be of value to anyone with an interest in banking and monetary policy, or Soviet and Eastern Bloc economies.

Maps of species' distributions or habitat suitability are required for many aspects of environmental research, resource management and conservation planning. These include biodiversity assessment, reserve design, habitat management and restoration, species and habitat conservation plans and predicting the effects of environmental change on species and ecosystems. The proliferation of methods and uncertainty regarding their effectiveness can be daunting to researchers, resource managers and conservation planners alike. Franklin summarises the methods used in species distribution modeling (also called niche modeling) and presents a framework for spatial prediction of species distributions based on the attributes (space, time, scale) of the data and questions being asked. The framework links theoretical ecological models of species distributions to spatial data on species and environment, and statistical models used for spatial prediction. Providing practical guidelines to students, resea

Engineers and scientists need powerful formalisms to make conceptual models of systems in order to analyse and design them. These models can be used to verify the behaviour of the systems, or as an executable specification of them. In this textbook, Professor van Hee concentrates on discrete dynamic systems, e.g. computer hardware, and information and logistical systems. He develops an integrated formalism which can be used as a prototyping language. It has three components: Petri nets, extended with time, token values and hierarchy; a specification language that is a subset of Z; and a binary data model, extended with complex objects. Much attention is paid to methods for constructing models of systems and analysing their behaviour, i.e. putting the theory into practice. The text is designed for use by advanced undergraduate and beginning graduate students, in computer science, electrical and industrial engineering, or applied mathematics; indeed, it is based on courses taught by the

This book contains an accessible discussion examining computationally-intensive techniques and bootstrap methods, providing ways to improve the finite-sample performance of well-known asymptotic tests

It is widely accepted among conservation biologists that genetics is, more than ever, an essential and efficient tool for wild and captive population management and reserve design. However, a true synergy between population genetics and conservation biology is lacking. Following the first International Workshop on Population Genetics for Animal Conservation in 2003 at the Centro di Ecologia Alpina, Trento, Italy (recently incorporated into the Edmund Mach Foundation), the scientific committee felt that, given the global urgency of animal conservation, it was imperative that discussions at the conference were made accessible to graduate students and wildlife managers. This book integrates 'the analytical methods approach' with the 'real problems approach' in conservation genetics. Each chapter is an exhaustive review of one area of expertise, and a special effort has been made to explain the statistical tools available for the analysis of molecular data as clearly as possible. The resul

The economic history of developing countries, particularly the former colonies, has become polarized between two ideologies. The apologists for colonialism have emphasized the stimulus given to the indigenous economy by the introduction of foreign capital; the 'underdevelopment theorists' have turned this interpretation on its head and represented the relationship as being, particularly in 'settler colonies' such as Kenya and Zimbabwe, one not of stimulus but of rape and plunder. In this study, Dr Mosley considers the economies of colonial Kenya and Southern Rhodesia and argues, in the light of recently assembled statistical data, that the truth is more complex than either of these simple interpretations allows. At the level of policy, most white producers acknowledged that they could not afford to let 'white mate black in a very few moves': they needed his cheap labour, cattle and maize too much to wish to damage seriously the peasant economy that sustained them.

This book discusses advanced statistical methods that can be used to analyse ecological data. Most environmental collected data are measured repeatedly over time, or space and this requires the use of

Multivariate methods are employed widely in the analysis of experimental data but are poorly understood by those users who are not statisticians. This is because of the wide divergence between the theory and practice of multivariate methods. This book provides concise yet thorough surveys of developments in multivariate statistical analysis and gives statistically sound coverage of the subject. The contributors are all experienced in the theory and practice of multivariate methods and their aim has been to emphasize the major features from the point of view of applicability and to indicate the limitations and conditions of the techniques. Professional statisticians wanting to improve their background in applicable methods, users of high-level statistical methods wanting to improve their background in fundamentals, and graduate students of statistics will all find this volume of value and use.

Monitoring Ecological Impacts provides the tools needed by professional ecologists, scientists, engineers, planners and managers to design assessment programs that can reliably monitor, detect and allow management of human impacts on the natural environment. The procedures described are well grounded in inferential logic, and the statistical models needed to analyse complex data are given. Step-by-step guidelines and flow diagrams provide the reader with clear and useable protocols, which can be applied in any region of the world and to a wide range of human impacts. In addition, real examples are used to show how the theory can be put into practice. Although the context of this book is flowing water environments, especially rivers and streams, the advice for designing assessment programs can be applied to any ecosystem.

This practical guide to survival data and its analysis for readers with a minimal background in statistics shows why the analytic methods work and how to effectively analyze and interpret epidemiologic and medical survival data with the help of modern computer systems. The introduction presents a review of a variety of statistical methods that are not only key elements of survival analysis but are also central to statistical analysis in general. Techniques such as statistical tests, transformations, confidence intervals, and analytic modeling are presented in the context of survival data but are, in fact, statistical tools that apply to understanding the analysis of many kinds of data. Similarly, discussions of such statistical concepts as bias, confounding, independence, and interaction are presented in the context of survival analysis and also are basic components of a broad range of applications. These topics make up essentially a 'second-year', one-semester biostatistics course in