With a foreword by Major-General Nico Geerts, Commander Netherlands Defence Academy, Breda, The Netherlands International conflict resolution increasingly involves the use of non-military power and non-kinetic capabilities alongside military capabilities in the face of hybrid threats. In this book, counter-measures to those threats are addressed by academics with both practical and theoretical experience and knowledge, providing strategic and operational insights into non-kinetic conflict resolution and on the use of power to influence, affect, deter or coerce states and non-state actors. This volume in the NL ARMS series deals with the non-kinetic capabilities to address international crises and conflicts and as always views matters from a global perspective. Included are chapters on the promise, practice and challenges of non-kinetic instruments of power, the instrumentality of soft power, information as a power instrument and manoeuvring in the information environment, Russia's use of deception and misinformation in conflict, applying counter-marketing techniques to fight ISIL, using statistics to profile terrorists, and employing tools such as Actor and Audience Analysis. Such diverse subjects as lawfare, the Law of Armed Conflict rules for non-kinetic cyber attacks, navigation warfare, GPS-spoofing, maritime interception operations, and finally, as a prerequisite, innovative ways for intelligence collection in UN Peacekeeping in Mali come up for discussion.The book will provide both professionals such as (foreign) policy makers and those active in the military services, academics at a master level and those with an interest in military law and the law of armed conflict with useful and up-to-date insights into the wide range of subjects that are contained within it. Paul A.L. Ducheine and Frans P.B. Osinga are General Officers and full professors at the Faculty of Military Sciences of the Netherlands Defence Academy in Breda, The Netherlands. Specific to this volume in the Series: * Written by academics with both practical and theoretical experience* Addresses counter measures to hybrid crises* Offers both strategic and operational insights to non-kinetic conflict resolution

This book includes the texts of the survey lectures given by plenary speakers at the 11th International ISAAC Congress held in Vaxjoe, Sweden, on 14-18 August, 2017. It is the purpose of ISAAC to promote analysis, its applications, and its interaction with computation. Analysis is understood here in the broad sense of the word, including differential equations, integral equations, functional analysis, and function theory. With this objective, ISAAC organizes international Congresses for the presentation and discussion of research on analysis. The plenary lectures in the present volume, authored by eminent specialists, are devoted to some exciting recent developments, topics including: local solvability for subprincipal type operators; fractional-order Laplacians; degenerate complex vector fields in the plane; lower bounds for pseudo-differential operators; a survey on Morrey spaces; localization operators in Signal Theory and Quantum Mechanics. Thanks to the accessible style used, readers only need a basic command of Calculus. This book will appeal to scientists, teachers, and graduate students in Mathematics, in particular Mathematical Analysis, Probability and Statistics, Numerical Analysis and Mathematical Physics.

Hawkes processes are studied and used in a wide range of disciplines: mathematics, social sciences, and earthquake modelling, to name a few. This book presents a selective coverage of the core and recent topics in the broad field of Hawkes processes. It consists of three parts. Parts I and II summarise and provide an overview of core theory (including key simulation methods) and inference methods, complemented by a selection of recent research developments and applications. Part III is devoted to case studies in seismology and finance that connect the core theory and inference methods to practical scenarios. This book is designed primarily for applied probabilists, statisticians, and machine learners. However, the mathematical prerequisites have been kept to a minimum so that the content will also be of interest to undergraduates in advanced mathematics and statistics, as well as machine learning practitioners. Knowledge of matrix theory with basics of probability theory, including Poisson processes, is considered a prerequisite. Colour-blind-friendly illustrations are included.

This is the standard textbook for courses on probability and statistics, not substantially updated. While helping students to develop their problem-solving skills, the author motivates students with practical applications from various areas of ECE that demonstrate the relevance of probability theory to engineering practice. Included are chapter overviews, summaries, checklists of important terms, annotated references, and a wide selection of fully worked-out real-world examples. In this edition, the Computer Methods sections have been updated and substantially enhanced and new problems have been added.

The book provides a comprehensive overview of the latest econometric methods for studying the dynamics of macroeconomic and financial time series. It examines alternative methodological approaches and concepts, including quantile spectra and co-spectra, and explores topics such as non-linear and non-stationary behavior, stochastic volatility models, and the econometrics of commodity markets and globalization. Furthermore, it demonstrates the application of recent techniques in various fields: in the frequency domain, in the analysis of persistent dynamics, in the estimation of state space models and new classes of volatility models. The book is divided into two parts: The first part applies econometrics to the field of macroeconomics, discussing trend/cycle decomposition, growth analysis, monetary policy and international trade. The second part applies econometrics to a wide range of topics in financial economics, including price dynamics in equity, commodity and foreign exchange markets and portfolio analysis. The book is essential reading for scholars, students, and practitioners in government and financial institutions interested in applying recent econometric time series methods to financial and economic data.

Modeling Uncertainty: An Examination of Stochastic Theory, Methods, and Applications, is a volume undertaken by the friends and colleagues of Sid Yakowitz in his honor. Fifty internionally known scholars have collectively contributed 30 papers on modeling uncertainty to this volume. Each of these papers was carefully reviewed and in the majority of cases the original submission was revised before being accepted for publication in the book. The papers cover a great variety of topics in probability, statistics, economics, stochastic optimization, control theory, regression analysis, simulation, stochastic programming, Markov decision process, application in the HIV context, and others. There are papers with a theoretical emphasis and others that focus on applications. A number of papers survey the work in a particular area and in a few papers the authors present their personal view of a topic. It is a book with a considerable number of expository articles, which are accessible to a nonexpert - a graduate student in mathematics, statistics, engineering, and economics departments, or just anyone with some mathematical background who is interested in a preliminary exposition of a particular topic. Many of the papers present the state of the art of a specific area or represent original contributions which advance the present state of knowledge. In sum, it is a book of considerable interest to a broad range of academic researchers and students of stochastic systems.

This text focuses on the use of smoothing methods for developing and estimating differential equations following recent developments in functional data analysis and building on techniques described in Ramsay and Silverman (2005) Functional Data Analysis. The central concept of a dynamical system as a buffer that translates sudden changes in input into smooth controlled output responses has led to applications of previously analyzed data, opening up entirely new opportunities for dynamical systems. The technical level has been kept low so that those with little or no exposure to differential equations as modeling objects can be brought into this data analysis landscape. There are already many texts on the mathematical properties of ordinary differential equations, or dynamic models, and there is a large literature distributed over many fields on models for real world processes consisting of differential equations. However, a researcher interested in fitting such a model to data, or a statistician interested in the properties of differential equations estimated from data will find rather less to work with. This book fills that gap.

This textbook introduces readers to practical statistical issues by presenting them within the context of real-life economics and business situations. It presents the subject in a non-threatening manner, with an emphasis on concise, easily understandable explanations. It has been designed to be accessible and student-friendly and, as an added learning feature, provides all the relevant data required to complete the accompanying exercises and computing problems, which are presented at the end of each chapter. It also discusses index numbers and inequality indices in detail, since these are of particular importance to students and commonly omitted in textbooks. Throughout the text it is assumed that the student has no prior knowledge of statistics. It is aimed primarily at business and economics undergraduates, providing them with the basic statistical skills necessary for further study of their subject. However, students of other disciplines will also find it relevant.

This book presents a comprehensive study of multivariate time series with linear state space structure. The emphasis is put on both the clarity of the theoretical concepts and on efficient algorithms for implementing the theory. In particular, it investigates the relationship between VARMA and state space models, including canonical forms. It also highlights the relationship between Wiener-Kolmogorov and Kalman filtering both with an infinite and a finite sample. The strength of the book also lies in the numerous algorithms included for state space models that take advantage of the recursive nature of the models. Many of these algorithms can be made robust, fast, reliable and efficient. The book is accompanied by a MATLAB package called SSMMATLAB and a webpage presenting implemented algorithms with many examples and case studies. Though it lays a solid theoretical foundation, the book also focuses on practical application, and includes exercises in each chapter. It is intended for researchers and students working with linear state space models, and who are familiar with linear algebra and possess some knowledge of statistics.

This book views multiple target tracking as a Bayesian inference problem. Within this framework it develops the theory of single target tracking, multiple target tracking, and likelihood ratio detection and tracking. In addition to providing a detailed description of a basic particle filter that implements the Bayesian single target recursion, this resource provides numerous examples that involve the use of particle filters. With these examples illustrating the developed concepts, algorithms, and approaches -- the book helps radar engineers track when observations are nonlinear functions of target site, when the target state distributions or measurement error distributions are not Gaussian, in low data rate and low signal to noise ratio situations, and when notions of contact and association are merged or unresolved among more than one target.

Statistical Techniques for Transportation Engineering is written with a systematic approach in mind and covers a full range of data analysis topics, from the introductory level (basic probability, measures of dispersion, random variable, discrete and continuous distributions) through more generally used techniques (common statistical distributions, hypothesis testing), to advanced analysis and statistical modeling techniques (regression, AnoVa, and time series). The book also provides worked out examples and solved problems for a wide variety of transportation engineering challenges.

1 Reference Material.- 1.1 Introduction.- 1.2 Singular Integral Equations.- 1.3 Improper Integrals.- 1.3.1 The Gamma function.- 1.3.2 The Beta function.- 1.3.3 Another important improper integral.- 1.3.4 A few integral identities.- 1.4 The Lebesgue Integral.- 1.5 Cauchy Principal Value for Integrals.- 1.6 The Hadamard Finite Part.- 1.7 Spaces of Functions and Distributions.- 1.8 Integral Transform Methods.- 1.8.1 Fourier transform.- 1.8.2 Laplace transform.- 1.9 Bibliographical Notes.- 2 Abel's and Related Integral Equations.- 2.1 Introduction.- 2.2 Abel's Equation.- 2.3 Related Integral Equations.- 2.4 The equation $$\int_{0}^{s} {{{{(s - t)}}^{\beta }}g(t)dt = f(s), \Re e \beta > - 1}$$.- 2.5 Path of Integration in the Complex Plane.- 2.6 The Equation $$\int_{{{ {C}_{{a\xi }}}}} {\frac{{g(z)dz}}{{ {{{(z - \xi )}}^{\nu }}}}} + k\int_{ {{{C}_{{\xi b}}}}} {\frac{ {g(z)dz}}{{{{{(\xi - z)}}^{\nu }}}}} = f(\xi )$$.- 2.7 Equations on a Closed Curve.- 2.8 Examples.- 2.9 Bibliographical Notes.- 2.10 Problems.- 3 Cauchy Type Integral Equations.- 3.1 Introduction.- 3.2 Cauchy Type Equation of the First Kind.- 3.3 An Alternative Approach.- 3.4 Cauchy Type Equations of the Second Kind.- 3.5 Cauchy Type Equations on a Closed Contour.- 3.6 Analytic Representation of Functions.- 3.7 Sectionally Analytic Functions (z?a)n?v(z?b)m+v.- 3.8 Cauchy's Integral Equation on an Open Contour.- 3.9 Disjoint Contours.- 3.10 Contours That Extend to Infinity.- 3.11 The Hilbert Kernel.- 3.12 The Hilbert Equation.- 3.13 Bibliographical Notes.- 3.14 Problems.- 4 Carleman Type Integral Equations.- 4.1 Introduction.- 4.2 Carleman Type Equation over a Real Interval.- 4.3 The Riemann-Hilbert Problem.- 4.4 Carleman Type Equations on a Closed Contour.- 4.5 Non-Normal Problems.- 4.6 A Factorization Procedure.- 4.7 An Operational Approach.- 4.8 Solution of a Related Integral Equation.- 4.9 Bibliographical Notes.- 4.10 Problems.- 5 Distributional Solutions of Singular Integral Equations.- 5.1 Introduction.- 5.2 Spaces of Generalized Functions.- 5.3 Generalized Solution of the Abel Equation.- 5.4 Integral Equations Related to Abel's Equation.- 5.5 The Fractional Integration Operators .- 5.6 The Cauchy Integral Equation over a Finite Interval.- 5.7 Analytic Representation of Distributions of ?'[a, b].- 5.8 Boundary Problems in A[a, b].- 5.9 Disjoint Intervals.- 5.9.1 The problem [RjF]j =hj.- 5.9.2 The equation A1?1(0F) + A2?2(F) = G.- 5.10 Equations Involving Periodic Distributions.- 5.11 Bibliographical Notes.- 5.12 Problems.- 6 Distributional Equations on the Whole Line.- 6.1 Introduction.- 6.2 Preliminaries.- 6.3 The Hilbert Transform of Distributions.- 6.4 Analytic Representation.- 6.5 Asymptotic Estimates.- 6.6 Distributional Solutions of Integral Equations.- 6.7 Non-Normal Equations.- 6.8 Bibliographical Notes.- 6.9 Problems.- 7 Integral Equations with Logarithmic Kernels.- 7.1 Introduction.- 7.2 Expansion of the Kernel In x-y.- 7.3 The Equation $$\int_{a}^{b} {\ln } \left {x - y} \rightg(y)dy = f(x)$$.- 7.4 Two Related Operators.- 7.5 Generalized Solutions of Equations with Logarithmic Kernels.- 7.6 The Operator $$\int_{a}^{b} {(P(x - y)\ln \left {x - y} \right + Q(x, y))g(y)dy}$$.- 7.7 Disjoint Intervals of Integration.- 7.8 An Equation Over a Semi-Infinite Interval.- 7.9 The Equation of the Second Kind Over a Semi-Infinite Interval.- 7.10 Asymptotic Behavior of Eigenvalues.- 7.11 Bibliographical Notes.- 7.12 Problems.- 8 Wiener-Hopf Integral Equations.- 8.1 Introduction.- 8.2 The Holomorphic Fourier Transform.- 8.3 The Mathematical Technique.- 8.4 The Distributional Wiener-Hopf Operators.- 8.5 Illustrations.- 8.6 Bibliographical Notes.- 8.7 Problems.- 9 Dual and Triple Integral Equations.- 9.1 Introduction.- 9.2 The Hankel Transform.- 9.3 Dual Equations with Trigonometric Kernels.- 9.4 Beltrami's Dual Integral Equations.- 9.5 Some Triple Integral Equations.- 9.6 Erdelyi-Koeber Operators.- 9.7 Dual Integral Equations of the Titchmarsh Type.- 9.8 D

This volume of selected and peer-reviewed contributions on the latest developments in time series analysis and forecasting updates the reader on topics such as analysis of irregularly sampled time series, multi-scale analysis of univariate and multivariate time series, linear and non-linear time series models, advanced time series forecasting methods, applications in time series analysis and forecasting, advanced methods and online learning in time series and high-dimensional and complex/big data time series. The contributions were originally presented at the International Work-Conference on Time Series, ITISE 2016, held in Granada, Spain, June 27-29, 2016. The series of ITISE conferences provides a forum for scientists, engineers, educators and students to discuss the latest ideas and implementations in the foundations, theory, models and applications in the field of time series analysis and forecasting. It focuses on interdisciplinary and multidisciplinary research encompassing the disciplines of computer science, mathematics, statistics and econometrics.

This book presents the latest research on the statistical analysis of functional, high-dimensional and other complex data, addressing methodological and computational aspects, as well as real-world applications. It covers topics like classification, confidence bands, density estimation, depth, diagnostic tests, dimension reduction, estimation on manifolds, high- and infinite-dimensional statistics, inference on functional data, networks, operatorial statistics, prediction, regression, robustness, sequential learning, small-ball probability, smoothing, spatial data, testing, and topological object data analysis, and includes applications in automobile engineering, criminology, drawing recognition, economics, environmetrics, medicine, mobile phone data, spectrometrics and urban environments. The book gathers selected, refereed contributions presented at the Fifth International Workshop on Functional and Operatorial Statistics (IWFOS) in Brno, Czech Republic. The workshop was originally to be held on June 24-26, 2020, but had to be postponed as a consequence of the COVID-19 pandemic. Initiated by the Working Group on Functional and Operatorial Statistics at the University of Toulouse in 2008, the IWFOS workshops provide a forum to discuss the latest trends and advances in functional statistics and related fields, and foster the exchange of ideas and international collaboration in the field.

This is author-approved bcc: This is the third volume of a collection of seminal papers in the statistical science written during the past 110 years. These papers have each had an outstanding influence on the development of statistical theory and practice over the last century. Each paper is preceded by an introduction written by an authority in the field providing background information and assessing its influence. Volume III concertrates on articles from the 1980's while including some earlier articles not included in Volumes I and II. Samuel Kotz is Professor of Statistics in the College of Business and Management at the University of Maryland. Norman L. Johnson is Professor Emeritus of Statistics at the University of North Carolina. Also available: Breakthroughs in Statistics Volume I: Foundations and Basic Theory Samuel Kotz and Norman L. Johnson, Editors 1993. 631 pp. Softcover. 0-387-94037-5 Breakthroughs in Statistics Volume II: Methodology and Distribution Samuel Kotz and Norman L. Johnson, Editors 1993. 600 pp. Softcover. 0-387-94039-1

Growth curve models in longitudinal studies are widely used to model population size, body height, biomass, fungal growth, and other variables in the biological sciences, but these statistical methods for modeling growth curves and analyzing longitudinal data also extend to general statistics, economics, public health, demographics, epidemiology, SQC, sociology, nano-biotechnology, fluid mechanics, and other applied areas. There is no one-size-fits-all approach to growth measurement. The selected papers in this volume build on presentations from the GCM workshop held at the Indian Statistical Institute, Giridih, on March 28-29, 2016. They represent recent trends in GCM research on different subject areas, both theoretical and applied. This book includes tools and possibilities for further work through new techniques and modification of existing ones. The volume includes original studies, theoretical findings and case studies from a wide range of applied work, and these contributions have been externally refereed to the high quality standards of leading journals in the field.

The purpose of this book is to thoroughly prepare the reader for applied research in clustering. Cluster analysis comprises a class of statistical techniques for classifying multivariate data into groups or clusters based on their similar features. Clustering is nowadays widely used in several domains of research, such as social sciences, psychology, and marketing, highlighting its multidisciplinary nature. This book provides an accessible and comprehensive introduction to clustering and offers practical guidelines for applying clustering tools by carefully chosen real-life datasets and extensive data analyses. The procedures addressed in this book include traditional hard clustering methods and up-to-date developments in soft clustering. Attention is paid to practical examples and applications through the open source statistical software R. Commented R code and output for conducting, step by step, complete cluster analyses are available. The book is intended for researchers interested in applying clustering methods. Basic notions on theoretical issues and on R are provided so that professionals as well as novices with little or no background in the subject will benefit from the book.

This book concentrates on linear regression, path analysis and logistic regressions, the most used statistical techniques for the test of causal relationships. Its emphasis is on the conceptions and applications of the techniques by using simple examples without requesting any mathematical knowledge. It shows multiple regression analysis accurately reconstructs the causal relationships between phenomena. So, it can be used to test the hypotheses about causal relationships between variables. It presents that potential effects of each independent variable on the dependent variable are not limited to direct and indirect effects. The path analysis shows each independent variable has a pure effect on the dependent variable. So, it can be shown the unique contribution of each independent variable to the variation of the dependent variable. It is an advanced statistical text for the graduate students in social and behavior sciences. It also serves as a reference for professionals and researchers.

This contributed volume applies spatial and space-time econometric methods to spatial interaction modeling. The first part of the book addresses general cutting-edge methodological questions in spatial econometric interaction modeling, which concern aspects such as coefficient interpretation, constrained estimation, and scale effects. The second part deals with technical solutions to particular estimation issues, such as intraregional flows, Bayesian PPML and VAR estimation. The final part presents a number of empirical applications, ranging from interregional tourism competition and domestic trade to space-time migration modeling and residential relocation.

This unique book develops the application of experimental statistical designs and analysis to discrete-event simulation modeling. It takes a practical perspective and orients the reader with examples of the role of simulation in modeling a system. The stages and steps for applying simulation are discussed by focusing on the important role of statistics. Examples are given about how to design an experiment using techniques such as classical designs, group screening, polynomial decomposition, and Taguchi designs. Using the statistical techniques discussed, a sound simulation model can be built and adequately tested before implementation.

The book also shows how simulation results can be generalized by discussing in full the growing emphasis on simulation metamodeling. Examples of this approach are presented to show that reliable and simple models could be easily obtained. Furthermore, such models are applied within a decision framework to optimize the system of interest. This expands the power of simulation from being purely descriptive of the system to being a prescriptive model. The reader is exposed to potential problems and how such problems may be harnessed. Although the book discusses statistical techniques, it is written so as to be comprehensible to anyone with a basic background in statistics. The book is a good resource for consultants and simulation practitioners; it can also be used as a textbook for classes in simulation.

This book presents modern Bayesian analysis in a format that is accessible to researchers in the fields of ecology, wildlife biology, and natural resource management. Bayesian analysis has undergone a remarkable transformation since the early 1990s. Widespread adoption of Markov chain Monte Carlo techniques has made the Bayesian paradigm the viable alternative to classical statistical procedures for scientific inference. The Bayesian approach has a number of desirable qualities, three chief ones being: i) the mathematical procedure is always the same, allowing the analyst to concentrate on the scientific aspects of the problem; ii) historical information is readily used, when appropriate; and iii) hierarchical models are readily accommodated. This monograph contains numerous worked examples and the requisite computer programs. The latter are easily modified to meet new situations. A primer on probability distributions is also included because these form the basis of Bayesian inference. Researchers and graduate students in Ecology and Natural Resource Management will find this book a valuable reference.

This book provides a concise introduction to the mathematical foundations of time series analysis, with an emphasis on mathematical clarity. The text is reduced to the essential logical core, mostly using the symbolic language of mathematics, thus enabling readers to very quickly grasp the essential reasoning behind time series analysis. It appeals to anybody wanting to understand time series in a precise, mathematical manner. It is suitable for graduate courses in time series analysis but is equally useful as a reference work for students and researchers alike.

6 Preliminaries.- 6.1 The operator of singular integration.- 6.2 The space Lp(?, ?).- 6.3 Singular integral operators.- 6.4 The spaces $$L_{p}^{ + }(\Gamma, \rho ), L_{p}^{ - }(\Gamma, \rho ) and \mathop{{L_{p}^{ - }}}\limits^{^\circ } (\Gamma, \rho )$$.- 6.5 Factorization.- 6.6 One-sided invertibility of singular integral operators.- 6.7 Fredholm operators.- 6.8 The local principle for singular integral operators.- 6.9 The interpolation theorem.- 7 General theorems.- 7.1 Change of the curve.- 7.2 The quotient norm of singular integral operators.- 7.3 The principle of separation of singularities.- 7.4 A necessary condition.- 7.5 Theorems on kernel and cokernel of singular integral operators.- 7.6 Two theorems on connections between singular integral operators.- 7.7 Index cancellation and approximative inversion of singular integral operators.- 7.8 Exercises.- Comments and references.- 8 The generalized factorization of bounded measurable functions and its applications.- 8.1 Sketch of the problem.- 8.2 Functions admitting a generalized factorization with respect to a curve in Lp(?, ?).- 8.3 Factorization in the spaces Lp(?, ?).- 8.4 Application of the factorization to the inversion of singular integral operators.- 8.5 Exercises.- Comments and references.- 9 Singular integral operators with piecewise continuous coefficients and their applications.- 9.1 Non-singular functions and their index.- 9.2 Criteria for the generalized factorizability of power functions.- 9.3 The inversion of singular integral operators on a closed curve.- 9.4 Composed curves.- 9.5 Singular integral operators with continuous coefficients on a composed curve.- 9.6 The case of the real axis.- 9.7 Another method of inversion.- 9.8 Singular integral operators with regel functions coefficients.- 9.9 Estimates for the norms of the operators P?, Q? and S?.- 9.10 Singular operators on spaces H?o(?, ?).- 9.11 Singular operators on symmetric spaces.- 9.12 Fredholm conditions in the case of arbitrary weights.- 9.13 Technical lemmas.- 9.14 Toeplitz and paired operators with piecewise continuous coefficients on the spaces lp and ?p.- 9.15 Some applications.- 9.16 Exercises.- Comments and references.- 10 Singular integral operators on non-simple curves.- 10.1 Technical lemmas.- 10.2 A preliminary theorem.- 10.3 The main theorem.- 10.4 Exercises.- Comments and references.- 11 Singular integral operators with coefficients having discontinuities of almost periodic type.- 11.1 Almost periodic functions and their factorization.- 11.2 Lemmas on functions with discontinuities of almost periodic type.- 11.3 The main theorem.- 11.4 Operators with continuous coefficients - the degenerate case.- 11.5 Exercises.- Comments and references.- 12 Singular integral operators with bounded measurable coefficients.- 12.1 Singular operators with measurable coefficients in the space L2(?).- 12.2 Necessary conditions in the space L2(?).- 12.3 Lemmas.- 12.4 Singular operators with coefficients in ?p(?). Sufficient conditions.- 12.5 The Helson-Szegoe theorem and its generalization.- 12.6 On the necessity of the condition a ? Sp.- 12.7 Extension of the class of coefficients.- 12.8 Exercises.- Comments and references.- 13 Exact constants in theorems on the boundedness of singular operators.- 13.1 Norm and quotient norm of the operator of singular integration.- 13.2 A second proof of Theorem 4.1 of Chapter 12.- 13.3 Norm and quotient norm of the operator S? on weighted spaces.- 13.4 Conditions for Fredholmness in spaces Lp(?, ?).- 13.5 Norms and quotient norm of the operator aI + bS?.- 13.6 Exercises.- Comments and references.- References.

The contributions in this book survey results on combinations of probabilistic and various other classical, temporal and justification logical systems. Formal languages of these logics are extended with probabilistic operators. The aim is to provide a systematic overview and an accessible presentation of mathematical techniques used to obtain results on formalization, completeness, compactness and decidability. The book will be of value to researchers in logic and it can be used as a supplementary text in graduate courses on non-classical logics.

This book features research contributions from The Abel Symposium on Statistical Analysis for High Dimensional Data, held in Nyvagar, Lofoten, Norway, in May 2014. The focus of the symposium was on statistical and machine learning methodologies specifically developed for inference in "big data" situations, with particular reference to genomic applications. The contributors, who are among the most prominent researchers on the theory of statistics for high dimensional inference, present new theories and methods, as well as challenging applications and computational solutions. Specific themes include, among others, variable selection and screening, penalised regression, sparsity, thresholding, low dimensional structures, computational challenges, non-convex situations, learning graphical models, sparse covariance and precision matrices, semi- and non-parametric formulations, multiple testing, classification, factor models, clustering, and preselection. Highlighting cutting-edge research and casting light on future research directions, the contributions will benefit graduate students and researchers in computational biology, statistics and the machine learning community.