- Table View
- List View
Theory of Nonparametric Tests
by Thorsten DickhausThis textbook provides a self-contained presentation of the main concepts and methods of nonparametric statistical testing, with a particular focus on the theoretical foundations of goodness-of-fit tests, rank tests, resampling tests, and projection tests. The substitution principle is employed as a unified approach to the nonparametric test problems discussed. In addition to mathematical theory, it also includes numerous examples and computer implementations. The book is intended for advanced undergraduate, graduate, and postdoc students as well as young researchers. Readers should be familiar with the basic concepts of mathematical statistics typically covered in introductory statistics courses.
Theory of Nonregular Factorial Designs (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)
by Ching-Shui Cheng Boxin TangNonregular factorial designs are a class of factorial designs that enable researchers to study simultaneously the effects of many explanatory variables on a response variable of interest. Factorial designs have been in the mainstream of design research for decades, but interest in nonregular factorial designs has been increasing significantly in the last twenty years or so. These designs are practically useful as they offer much richer and more flexible choices for experimenters to contemplate than regular designs. Theoretically, they present new challenges and raise new issues that require new ideas and methods to properly address. This book is the first to synthesize research on this topic into a consistent statistical design framework. It is focused on the theoretical underpinnings of nonregular factorial designs but emphasizes the ideas and methods of statistical designs, rather than mathematical rigor. Some background is provided in an appendix, and the book also includes lots of worked examples to illustrate the ideas and methods.Features: A unified and comprehensive overview of the theory and application of nonregular factorial designs Self-contained, with an appendix of necessary background knowledge in factorial designs Mathematics is kept to a minimum, with proofs only included if simple or insightful, or is otherwise in chapter appendices Great attention has been paid to the narrative flow Lots of worked examples to illustrate the ideas and methods This book is primarily aimed at researchers and practitioners working on the design of experiments. It will be a perfect entry into the field for a junior researcher and a useful reference for all researchers already in the field. It could be used as a textbook for graduate students who have taken a first design course and have a passion for design ideas and research. Scientists from other disciplines will find the book useful as well, if their research involves multi-factor experiments.
Theory of Np Spaces (Frontiers in Mathematics)
by Javad Mashreghi Le Hai KhoiThis monograph provides a comprehensive study of a typical and novel function space, known as the $\mathcal{N}_p$ spaces. These spaces are Banach and Hilbert spaces of analytic functions on the open unit disk and open unit ball, and the authors also explore composition operators and weighted composition operators on these spaces. The book covers a significant portion of the recent research on these spaces, making it an invaluable resource for those delving into this rapidly developing area. The authors introduce various weighted spaces, including the classical Hardy space $H^2$, Bergman space $B^2$, and Dirichlet space $\mathcal{D}$. By offering generalized definitions for these spaces, readers are equipped to explore further classes of Banach spaces such as Bloch spaces $\mathcal{B}^p$ and Bergman-type spaces $A^p$. Additionally, the authors extend their analysis beyond the open unit disk $\mathbb{D}$ and open unit ball $\mathbb{B}$ by presenting families of entire functions in the complex plane $\mathbb{C}$ and in higher dimensions. The Theory of $\mathcal{N}_p$ Spaces is an ideal resource for researchers and PhD students studying spaces of analytic functions and operators within these spaces.
Theory of Phase Transitions in Polypeptides and Proteins
by Alexander V. YakubovichThere are nearly 100 000 different protein sequences encoded in the human genome, each with its own specific fold. Understanding how a newly formed polypeptide sequence finds its way to the correct fold is one of the greatest challenges in the modern structural biology. The aim of this thesis is to provide novel insights into protein folding by considering the problem from the point of view of statistical mechanics. The thesis starts by investigating the fundamental degrees of freedom in polypeptides that are responsible for the conformational transitions. This knowledge is then applied in the statistical mechanics description of helix coil transitions in polypeptides. Finally, the theoretical formalism is generalized to the case of proteins in an aqueous environment. The major novelty of this work lies in combining (a) a formalism based on fundamental physical properties of the system and (b) the resulting possibility of describing the folding unfolding transitions quantitatively. The clear physical nature of the formalism opens the way to further applications in a large variety of systems and processes.
Theory of Probability
by Boris V. GnedenkoThis book is the sixth edition of a classic text that was first published in 1950 in the former Soviet Union. The clear presentation of the subject and extensive applications supported with real data helped establish the book as a standard for the field. To date, it has been published into more that ten languages and has gone through five editions. The sixth edition is a major revision over the fifth. It contains new material and results on the Local Limit Theorem, the Integral Law of Large Numbers, and Characteristic Functions. The new edition retains the feature of developing the subject from intuitive concepts and demonstrating techniques and theory through large numbers of examples. The author has, for the first time, included a brief history of probability and its development. Exercise problems and examples have been revised and new ones added.
Theory of Probability and Random Processes
by Yakov G. Sinai Leonid KoralovA one-year course in probability theory and the theory of random processes, taught at Princeton University to undergraduate and graduate students, forms the core of this book. It provides a comprehensive and self-contained exposition of classical probability theory and the theory of random processes. The book includes detailed discussion of Lebesgue integration, Markov chains, random walks, laws of large numbers, limit theorems, and their relation to Renormalization Group theory. It also includes the theory of stationary random processes, martingales, generalized random processes, and Brownian motion.
Theory of Probability: A critical introductory treatment
by Bruno De FinettiFirst issued in translation as a two-volume work in 1975, this classic book provides the first complete development of the theory of probability from a subjectivist viewpoint. It proceeds from a detailed discussion of the philosophical mathematical aspects to a detailed mathematical treatment of probability and statistics. De Finetti’s theory of probability is one of the foundations of Bayesian theory. De Finetti stated that probability is nothing but a subjective analysis of the likelihood that something will happen and that that probability does not exist outside the mind. It is the rate at which a person is willing to bet on something happening. This view is directly opposed to the classicist/ frequentist view of the likelihood of a particular outcome of an event, which assumes that the same event could be identically repeated many times over, and the 'probability' of a particular outcome has to do with the fraction of the time that outcome results from the repeated trials.
Theory of Random Sets
by Ilya MolchanovStochastic geometry is a relatively new branch of mathematics. Although its predecessors such as geometric probability date back to the 18th century, the formal concept of a random set was developed in the beginning of the 1970s. Theory of Random Sets presents a state of the art treatment of the modern theory, but it does not neglect to recall and build on the foundations laid by Matheron and others, including the vast advances in stochastic geometry, probability theory, set-valued analysis, and statistical inference of the 1990s. The book is entirely self-contained, systematic and exhaustive, with the full proofs that are necessary to gain insight. It shows the various interdisciplinary relationships of random set theory within other parts of mathematics, and at the same time, fixes terminology and notation that are often varying in the current literature to establish it as a natural part of modern probability theory, and to provide a platform for future development.
Theory of Reproducing Kernels and Applications
by Saburou Saitoh Yoshihiro SawanoThis book provides a large extension of the general theory of reproducing kernels published by N. Aronszajn in 1950, with many concrete applications. In Chapter 1, many concrete reproducing kernels are first introduced with detailed information. Chapter 2 presents a general and global theory of reproducing kernels with basic applications in a self-contained way. Many fundamental operations among reproducing kernel Hilbert spaces are dealt with. Chapter 2 is the heart of this book. Chapter 3 is devoted to the Tikhonov regularization using the theory of reproducing kernels with applications to numerical and practical solutions of bounded linear operator equations. In Chapter 4, the numerical real inversion formulas of the Laplace transform are presented by applying the Tikhonov regularization, where the reproducing kernels play a key role in the results. Chapter 5 deals with ordinary differential equations; Chapter 6 includes many concrete results for various fundamental partial differential equations. In Chapter 7, typical integral equations are presented with discretization methods. These chapters are applications of the general theories of Chapter 3 with the purpose of practical and numerical constructions of the solutions. In Chapter 8, hot topics on reproducing kernels are presented; namely, norm inequalities, convolution inequalities, inversion of an arbitrary matrix, representations of inverse mappings, identifications of nonlinear systems, sampling theory, statistical learning theory and membership problems. Relationships among eigen-functions, initial value problems for linear partial differential equations, and reproducing kernels are also presented. Further, new fundamental results on generalized reproducing kernels, generalized delta functions, generalized reproducing kernel Hilbert spaces, andas well, a general integral transform theory are introduced. In three Appendices, the deep theory of Akira Yamada discussing the equality problems in nonlinear norm inequalities, Yamada's unified and generalized inequalities for Opial's inequalities and the concrete and explicit integral representation of the implicit functions are presented.
Theory of Ridge Regression Estimation with Applications (Wiley Series in Probability and Statistics)
by Mohammad Arashi A. K. Saleh Golam KibriaA guide to the systematic analytical results for ridge, LASSO, preliminary test, and Stein-type estimators with applications Theory of Ridge Regression Estimation with Applications offers a comprehensive guide to the theory and methods of estimation. Ridge regression and LASSO are at the center of all penalty estimators in a range of standard models that are used in many applied statistical analyses. Written by noted experts in the field, the book contains a thorough introduction to penalty and shrinkage estimation and explores the role that ridge, LASSO, and logistic regression play in the computer intensive area of neural network and big data analysis. Designed to be accessible, the book presents detailed coverage of the basic terminology related to various models such as the location and simple linear models, normal and rank theory-based ridge, LASSO, preliminary test and Stein-type estimators. The authors also include problem sets to enhance learning. This book is a volume in the Wiley Series in Probability and Statistics series that provides essential and invaluable reading for all statisticians. This important resource: Offers theoretical coverage and computer-intensive applications of the procedures presented Contains solutions and alternate methods for prediction accuracy and selecting model procedures Presents the first book to focus on ridge regression and unifies past research with current methodology Uses R throughout the text and includes a companion website containing convenient data sets Written for graduate students, practitioners, and researchers in various fields of science, Theory of Ridge Regression Estimation with Applications is an authoritative guide to the theory and methodology of statistical estimation.
Theory of Sampling and Sampling Practice, Third Edition
by Francis F. PitardA step-by-step guide for anyone challenged by the many subtleties of sampling particulate materials. The only comprehensive document merging the famous works of P. Gy, I. Visman, and C.O. Ingamells into a single theory in a logical way - the most advanced book on sampling that can be used by all sampling practitioners around the world.
Theory of Science and Technology Transfer and Applications
by Sifeng Liu Zhigeng Fang Hongxing Shi Benhai GuoConstructive Suggestions for Efficiently Implementing Technology Transfer Theory of Science and Technology Transfer and Applications presents the mechanisms, features, effects, and modes of technology transfer. It addresses the measurement, cost, benefit, optimal allocation, and game theory of technology transfer, along with the dynamics of the tec
Theory of Spatial Statistics: A Concise Introduction (Chapman & Hall/CRC Texts in Statistical Science)
by M.N.M. van LieshoutTheory of Spatial Statistics: A Concise Introduction presents the most important models used in spatial statistics, including random fields and point processes, from a rigorous mathematical point of view and shows how to carry out statistical inference. It contains full proofs, real-life examples and theoretical exercises. Solutions to the latter are available in an appendix.Assuming maturity in probability and statistics, these concise lecture notes are self-contained and cover enough material for a semester course. They may also serve as a reference book for researchers.Features* Presents the mathematical foundations of spatial statistics.* Contains worked examples from mining, disease mapping, forestry, soil and environmental science, and criminology.* Gives pointers to the literature to facilitate further study.* Provides example code in R to encourage the student to experiment.* Offers exercises and their solutions to test and deepen understanding.The book is suitable for postgraduate and advanced undergraduate students in mathematics and statistics.
Theory of Spinors and Its Application in Physics and Mechanics
by Vladimir A. ZhelnorovichThis book contains a systematic exposition of the theory of spinors in finite-dimensional Euclidean and Riemannian spaces. The applications of spinors in field theory and relativistic mechanics of continuous media are considered. The main mathematical part is connected with the study of invariant algebraic and geometric relations between spinors and tensors. The theory of spinors and the methods of the tensor representation of spinors and spinor equations are thoroughly expounded in four-dimensional and three-dimensional spaces. Very useful and important relations are derived that express the derivatives of the spinor fields in terms of the derivatives of various tensor fields.The problems associated with an invariant description of spinors as objects that do not depend on the choice of a coordinate system are addressed in detail. As an application, the author considers an invariant tensor formulation of certain classes of differential spinor equations containing, in particular, the most important spinor equations of field theory and quantum mechanics. Exact solutions of the Einstein–Dirac equations, nonlinear Heisenberg’s spinor equations, and equations for relativistic spin fluids are given. The book presents a large body of factual material and is suited for use as a handbook. It is intended for specialists in theoretical physics, as well as for students and post-graduate students of physical and mathematical specialties.
Theory of Stability of Continuous Elastic Structures (Engineering Mathematics Ser.)
by Mario ComoTheory of Stability of Continuous Elastic Structures presents an applied mathematical treatment of the stability of civil engineering structures. The book's modern and rigorous approach makes it especially useful as a text in advanced engineering courses and an invaluable reference for engineers.
Theory of Stabilization for Linear Boundary Control Systems
by Takao NambuThis book presents a unified algebraic approach to stabilization problems of linear boundary control systems with no assumption on finite-dimensional approximations to the original systems, such as the existence of the associated Riesz basis. A new proof of the stabilization result for linear systems of finite dimension is also presented, leading to an explicit design of the feedback scheme. The problem of output stabilization is discussed, and some interesting results are developed when the observability or the controllability conditions are not satisfied.
Theory of Statistical Inference (Chapman & Hall/CRC Texts in Statistical Science)
by Anthony AlmudevarTheory of Statistical Inference is designed as a reference on statistical inference for researchers and students at the graduate or advanced undergraduate level. It presents a unified treatment of the foundational ideas of modern statistical inference, and would be suitable for a core course in a graduate program in statistics or biostatistics. The emphasis is on the application of mathematical theory to the problem of inference, leading to an optimization theory allowing the choice of those statistical methods yielding the most efficient use of data. The book shows how a small number of key concepts, such as sufficiency, invariance, stochastic ordering, decision theory and vector space algebra play a recurring and unifying role. The volume can be divided into four sections. Part I provides a review of the required distribution theory. Part II introduces the problem of statistical inference. This includes the definitions of the exponential family, invariant and Bayesian models. Basic concepts of estimation, confidence intervals and hypothesis testing are introduced here. Part III constitutes the core of the volume, presenting a formal theory of statistical inference. Beginning with decision theory, this section then covers uniformly minimum variance unbiased (UMVU) estimation, minimum risk equivariant (MRE) estimation and the Neyman-Pearson test. Finally, Part IV introduces large sample theory. This section begins with stochastic limit theorems, the δ-method, the Bahadur representation theorem for sample quantiles, large sample U-estimation, the Cramér-Rao lower bound and asymptotic efficiency. A separate chapter is then devoted to estimating equation methods. The volume ends with a detailed development of large sample hypothesis testing, based on the likelihood ratio test (LRT), Rao score test and the Wald test. Features This volume includes treatment of linear and nonlinear regression models, ANOVA models, generalized linear models (GLM) and generalized estimating equations (GEE). An introduction to decision theory (including risk, admissibility, classification, Bayes and minimax decision rules) is presented. The importance of this sometimes overlooked topic to statistical methodology is emphasized. The volume emphasizes throughout the important role that can be played by group theory and invariance in statistical inference. Nonparametric (rank-based) methods are derived by the same principles used for parametric models and are therefore presented as solutions to well-defined mathematical problems, rather than as robust heuristic alternatives to parametric methods. Each chapter ends with a set of theoretical and applied exercises integrated with the main text. Problems involving R programming are included. Appendices summarize the necessary background in analysis, matrix algebra and group theory.
Theory of Stochastic Integrals
by Jorge A. LeónIn applications of stochastic calculus, there are phenomena that cannot be analyzed through the classical Itô theory. It is necessary, therefore, to have a theory based on stochastic integration with respect to these situations.Theory of Stochastic Integrals aims to provide the answer to this problem by introducing readers to the study of some interpretations of stochastic integrals with respect to stochastic processes that are not necessarily semimartingales, such as Volterra Gaussian processes, or processes with bounded p-variation among which we can mention fractional Brownian motion and Riemann-Liouville fractional process.Features Self-contained treatment of the topic Suitable as a teaching or research tool for those interested in stochastic analysis and its applications Includes original results.
Theory of Stochastic Objects: Probability, Stochastic Processes and Inference (Chapman & Hall/CRC Texts in Statistical Science)
by Athanasios Christou MicheasThis book defines and investigates the concept of a random object. To accomplish this task in a natural way, it brings together three major areas; statistical inference, measure-theoretic probability theory and stochastic processes. This point of view has not been explored by existing textbooks; one would need material on real analysis, measure and probability theory, as well as stochastic processes - in addition to at least one text on statistics- to capture the detail and depth of material that has gone into this volume. Presents and illustrates ‘random objects’ in different contexts, under a unified framework, starting with rudimentary results on random variables and random sequences, all the way up to stochastic partial differential equations. Reviews rudimentary probability and introduces statistical inference, from basic to advanced, thus making the transition from basic statistical modeling and estimation to advanced topics more natural and concrete. Compact and comprehensive presentation of the material that will be useful to a reader from the mathematics and statistical sciences, at any stage of their career, either as a graduate student, an instructor, or an academician conducting research and requiring quick references and examples to classic topics. Includes 378 exercises, with the solutions manual available on the book's website. 121 illustrative examples of the concepts presented in the text (many including multiple items in a single example). The book is targeted towards students at the master’s and Ph.D. levels, as well as, academicians in the mathematics, statistics and related disciplines. Basic knowledge of calculus and matrix algebra is required. Prior knowledge of probability or measure theory is welcomed but not necessary.
Theory of Stochastic Processes
by Dmytro Gusak Andrey Pilipenko Alexander Kukush Yuliya Mishura Alexey KulikThis book is a collection of exercises covering all the main topics in the modern theory of stochastic processes and its applications, including finance, actuarial mathematics, queuing theory, and risk theory. The aim of this book is to provide the reader with the theoretical and practical material necessary for deeper understanding of the main topics in the theory of stochastic processes and its related fields. The book is divided into chapters according to the various topics. Each chapter contains problems, hints, solutions, as well as a self-contained theoretical part which gives all the necessary material for solving the problems. References to the literature are also given. The exercises have various levels of complexity and vary from simple ones, useful for students studying basic notions and technique, to very advanced ones that reveal some important theoretical facts and constructions. This book is one of the largest collections of problems in the theory of stochastic processes and its applications. The problems in this book can be useful for undergraduate and graduate students, as well as for specialists in the theory of stochastic processes.
Theory of Thermodynamic Measurements of Quantum Systems Far from Equilibrium (Springer Theses)
by Abhay ShastryThis thesis presents several related advances in the field of nonequilibrium quantum thermodynamics. The central result is an ingenious proof that the local temperature and voltage measurement in a nonequilibrium system of fermions exists and is unique, placing the concept of local temperature on a rigorous mathematical footing for the first time. As an intermediate step, a proof of the positivity of the Onsager matrix of linear response theory is given -- a statement of the second law of thermodynamics that had lacked an independent proof for 85 years. A new experimental method to measure the local temperature of an electron system using purely electrical techniques is also proposed, which could enable improvements to the spatial resolution of thermometry by several orders of magnitude. Finally, a new mathematically-exact definition for the local entropy of a quantum system in a nonequilibrium steady state is derived. Several different measures of the local entropy are discussed, relating to the thermodynamics of processes that a local observer with varying degrees of information about the microstates of the system could carry out, and it is shown that they satisfy a hierarchy of inequalities. Proofs of the third law of thermodynamics for generic open quantum systems are presented, taking into account the entropic contribution due to localized states. Appropriately normalized (per-state) local entropies are defined and are used to quantify the departure from local equilibrium.
Theory of Third-Order Differential Equations
by Seshadev Padhi Smita PatiThis book discusses the theory of third-order differential equations. Most of the results are derived from the results obtained for third-order linear homogeneous differential equations with constant coefficients. M. Gregus, in his book written in 1987, only deals with third-order linear differential equations. These findings are old, and new techniques have since been developed and new results obtained. Chapter 1 introduces the results for oscillation and non-oscillation of solutions of third-order linear differential equations with constant coefficients, and a brief introduction to delay differential equations is given. The oscillation and asymptotic behavior of non-oscillatory solutions of homogeneous third-order linear differential equations with variable coefficients are discussed in Ch. 2. The results are extended to third-order linear non-homogeneous equations in Ch. 3, while Ch. 4 explains the oscillation and non-oscillation results for homogeneous third-order nonlinear differential equations. Chapter 5 deals with the z-type oscillation and non-oscillation of third-order nonlinear and non-homogeneous differential equations. Chapter 6 is devoted to the study of third-order delay differential equations. Chapter 7 explains the stability of solutions of third-order equations. Some knowledge of differential equations, analysis and algebra is desirable, but not essential, in order to study the topic.
Theory of Translation Closedness for Time Scales: With Applications in Translation Functions and Dynamic Equations (Developments in Mathematics #62)
by Ravi P. Agarwal Chao Wang Rathinasamy Sakthivel Donal O' ReganThis monograph establishes a theory of classification and translation closedness of time scales, a topic that was first studied by S. Hilger in 1988 to unify continuous and discrete analysis. The authors develop a theory of translation function on time scales that contains (piecewise) almost periodic functions, (piecewise) almost automorphic functions and their related generalization functions (e.g., pseudo almost periodic functions, weighted pseudo almost automorphic functions, and more). Against the background of dynamic equations, these function theories on time scales are applied to study the dynamical behavior of solutions for various types of dynamic equations on hybrid domains, including evolution equations, discontinuous equations and impulsive integro-differential equations.The theory presented allows many useful applications, such as in the Nicholson`s blowfiles model; the Lasota-Wazewska model; the Keynesian-Cross model; in those realistic dynamical models with a more complex hibrid domain, considered under different types of translation closedness of time scales; and in dynamic equations on mathematical models which cover neural networks. This book provides readers with the theoretical background necessary for accurate mathematical modeling in physics, chemical technology, population dynamics, biotechnology and economics, neural networks, and social sciences.
Theory of the Spread of Epidemics and Movement Ecology of Animals: An Interdisciplinary Approach using Methodologies of Physics and Mathematics
by V. M. Kenkre Luca GiuggioliExploiting powerful techniques from physics and mathematics, this book studies animal movement in ecology, with a focus on epidemic spread. Pulmonary syndrome is not only feared in epidemics of recent times, such as COVID-19, but is also characteristic of epidemics studied earlier such as Hantavirus. The Hantavirus is one of the book's central topics. Correlations between epidemic outbreaks and precipitation events like El Niño are analyzed and spatial reservoirs of infection in off-period of the epidemic, known as refugia, are studied. Predicted traveling waves of infection are successfully compared to field observations. Territoriality in scent-marking animals is presented, with parallels drawn with the theory of melting. The flocking and herding of birds and mammals are described in terms of collective excitations. For scientists interested in movement ecology and epidemic spread, this book provides effective solutions to long-standing problems.
Theory, Formulation and Realization of Artifacts Science: 3M&I-Body System (SpringerBriefs in Business)
by Masayuki MatsuiThis book considers and builds on the main propositions regarding body similarity and the principles of nature versus artifacts in science. It also explores the design (matrix) power of the human, Material/Machine, Money & Information (3M&I) body with respect to productivity/gross domestic product (GDP). The book begins in 2009 with Weiner’s cybernetics and describes Matsui’s theory and dynamism concerning the basic equation of W = ZL and artifact formulation using matrix methods, such as Matsui’s matrix equation (Matsui’s ME). In his book Fundamentals and Principles of Artifacts Science: 3M&I-Body System, published by Springer in 2016, the author championed the white-box approach for 3M&I artifacts in contrast to Simon’s artificial approach from 1969. Two principles, the Sandwich (waist) and Balancing theories, and their fundamental problems, were identified. This book now proposes a third principle: the fractal/harmonic-like structure of the cosmos and life types in space and time. The book further elaborates on the complexity of the 3M&I system and management in terms of enterprises, economics, nature, and other applications. Also, the domain of nature versus artifacts is highlighted, demonstrating the possibility of a white-box cybernetics-type robot. This fosters the realization of humanized and harmonic worlds that combine increased happiness and social productivity in an age increasingly dominated by technology.