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Statistical Methods in Diagnostic Medicine (Wiley Series in Probability and Statistics #569)
by Xiao-Hua Zhou Donna K. McClish Nancy A. ObuchowskiAn important role of diagnostic medicine research is to estimate and compare the accuracies of diagnostic tests. This book provides a comprehensive account of statistical methods for design and analysis of diagnostic studies, including sample size calculations, estimation of the accuracy of a diagnostic test, comparison of accuracies of competing diagnostic tests, and regression analysis of diagnostic accuracy data. Discussing recently developed methods for correction of verification bias and imperfect reference bias, methods for analysis of clustered diagnostic accuracy data, and meta-analysis methods, Statistical Methods in Diagnostic Medicine explains: * Common measures of diagnostic accuracy and designs for diagnostic accuracy studies * Methods of estimation and hypothesis testing of the accuracy of diagnostic tests * Meta-analysis * Advanced analytic techniques-including methods for comparing correlated ROC curves in multi-reader studies, correcting verification bias, and correcting when an imperfect gold standard is used Thoroughly detailed with numerous applications and end-of-chapter problems as well as a related FTP site providing FORTRAN program listings, data sets, and instructional hints, Statistical Methods in Diagnostic Medicine is a valuable addition to the literature of the field, serving as a much-needed guide for both clinicians and advanced students.
Statistical Methods in Diagnostic Medicine (Wiley Series in Probability and Statistics #712)
by Xiao-Hua Zhou Nancy A. Obuchowski Donna K. McClishPraise for the First Edition " . . . the book is a valuable addition to the literature in the field, serving as a much-needed guide for both clinicians and advanced students."—Zentralblatt MATH A new edition of the cutting-edge guide to diagnostic tests in medical research In recent years, a considerable amount of research has focused on evolving methods for designing and analyzing diagnostic accuracy studies. Statistical Methods in Diagnostic Medicine, Second Edition continues to provide a comprehensive approach to the topic, guiding readers through the necessary practices for understanding these studies and generalizing the results to patient populations. Following a basic introduction to measuring test accuracy and study design, the authors successfully define various measures of diagnostic accuracy, describe strategies for designing diagnostic accuracy studies, and present key statistical methods for estimating and comparing test accuracy. Topics new to the Second Edition include: Methods for tests designed to detect and locate lesions Recommendations for covariate-adjustment Methods for estimating and comparing predictive values and sample size calculations Correcting techniques for verification and imperfect standard biases Sample size calculation for multiple reader studies when pilot data are available Updated meta-analysis methods, now incorporating random effects Three case studies thoroughly showcase some of the questions and statistical issues that arise in diagnostic medicine, with all associated data provided in detailed appendices. A related web site features Fortran, SAS®, and R software packages so that readers can conduct their own analyses. Statistical Methods in Diagnostic Medicine, Second Edition is an excellent supplement for biostatistics courses at the graduate level. It also serves as a valuable reference for clinicians and researchers working in the fields of medicine, epidemiology, and biostatistics.
Statistical Methods in Diagnostic Medicine (Wiley Series in Probability and Statistics #712)
by Xiao-Hua Zhou Nancy A. Obuchowski Donna K. McClishPraise for the First Edition " . . . the book is a valuable addition to the literature in the field, serving as a much-needed guide for both clinicians and advanced students."—Zentralblatt MATH A new edition of the cutting-edge guide to diagnostic tests in medical research In recent years, a considerable amount of research has focused on evolving methods for designing and analyzing diagnostic accuracy studies. Statistical Methods in Diagnostic Medicine, Second Edition continues to provide a comprehensive approach to the topic, guiding readers through the necessary practices for understanding these studies and generalizing the results to patient populations. Following a basic introduction to measuring test accuracy and study design, the authors successfully define various measures of diagnostic accuracy, describe strategies for designing diagnostic accuracy studies, and present key statistical methods for estimating and comparing test accuracy. Topics new to the Second Edition include: Methods for tests designed to detect and locate lesions Recommendations for covariate-adjustment Methods for estimating and comparing predictive values and sample size calculations Correcting techniques for verification and imperfect standard biases Sample size calculation for multiple reader studies when pilot data are available Updated meta-analysis methods, now incorporating random effects Three case studies thoroughly showcase some of the questions and statistical issues that arise in diagnostic medicine, with all associated data provided in detailed appendices. A related web site features Fortran, SAS®, and R software packages so that readers can conduct their own analyses. Statistical Methods in Diagnostic Medicine, Second Edition is an excellent supplement for biostatistics courses at the graduate level. It also serves as a valuable reference for clinicians and researchers working in the fields of medicine, epidemiology, and biostatistics.
Applied Missing Data Analysis in the Health Sciences (Statistics in Practice #81)
by Xiao-Hua Zhou Chuan Zhou Danping Lui Xaiobo DingA modern and practical guide to the essential concepts and ideas for analyzing data with missing observations in the field of biostatistics With an emphasis on hands-on applications, Applied Missing Data Analysis in the Health Sciences outlines the various modern statistical methods for the analysis of missing data. The authors acknowledge the limitations of established techniques and provide newly-developed methods with concrete applications in areas such as causal inference methods and the field of diagnostic medicine. Organized by types of data, chapter coverage begins with an overall introduction to the existence and limitations of missing data and continues into traditional techniques for missing data inference, including likelihood-based, weighted GEE, multiple imputation, and Bayesian methods. The book’s subsequently covers cross-sectional, longitudinal, hierarchical, survival data. In addition, Applied Missing Data Analysis in the Health Sciences features: Multiple data sets that can be replicated using the SAS®, Stata®, R, and WinBUGS software packages Numerous examples of case studies in the field of biostatistics to illustrate real-world scenarios and demonstrate applications of discussed methodologies Detailed appendices to guide readers through the use of the presented data in various software environments Applied Missing Data Analysis in the Health Sciences is an excellent textbook for upper-undergraduate and graduate-level biostatistics courses as well as an ideal resource for health science researchers and applied statisticians.
Applied Missing Data Analysis in the Health Sciences (Statistics in Practice)
by Xiao-Hua Zhou Chuan Zhou Danping Lui Xaiobo DingA modern and practical guide to the essential concepts and ideas for analyzing data with missing observations in the field of biostatistics With an emphasis on hands-on applications, Applied Missing Data Analysis in the Health Sciences outlines the various modern statistical methods for the analysis of missing data. The authors acknowledge the limitations of established techniques and provide newly-developed methods with concrete applications in areas such as causal inference methods and the field of diagnostic medicine. Organized by types of data, chapter coverage begins with an overall introduction to the existence and limitations of missing data and continues into traditional techniques for missing data inference, including likelihood-based, weighted GEE, multiple imputation, and Bayesian methods. The book’s subsequently covers cross-sectional, longitudinal, hierarchical, survival data. In addition, Applied Missing Data Analysis in the Health Sciences features: Multiple data sets that can be replicated using the SAS®, Stata®, R, and WinBUGS software packages Numerous examples of case studies in the field of biostatistics to illustrate real-world scenarios and demonstrate applications of discussed methodologies Detailed appendices to guide readers through the use of the presented data in various software environments Applied Missing Data Analysis in the Health Sciences is an excellent textbook for upper-undergraduate and graduate-level biostatistics courses as well as an ideal resource for health science researchers and applied statisticians.
Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection
by Xuefeng Zhou Hongmin Wu Juan Rojas Zhihao Xu Shuai LiThis open access book focuses on robot introspection, which has a direct impact on physical human–robot interaction and long-term autonomy, and which can benefit from autonomous anomaly monitoring and diagnosis, as well as anomaly recovery strategies. In robotics, the ability to reason, solve their own anomalies and proactively enrich owned knowledge is a direct way to improve autonomous behaviors. To this end, the authors start by considering the underlying pattern of multimodal observation during robot manipulation, which can effectively be modeled as a parametric hidden Markov model (HMM). They then adopt a nonparametric Bayesian approach in defining a prior using the hierarchical Dirichlet process (HDP) on the standard HMM parameters, known as the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM). The HDP-HMM can examine an HMM with an unbounded number of possible states and allows flexibility in the complexity of the learned model and the development of reliable and scalable variational inference methods.This book is a valuable reference resource for researchers and designers in the field of robot learning and multimodal perception, as well as for senior undergraduate and graduate university students.
Topological Structure of the Solution Set for Evolution Inclusions (Developments in Mathematics #51)
by Yong Zhou Rong-Nian Wang Li PengThis book systematically presents the topological structure of solution sets and attractability for nonlinear evolution inclusions, together with its relevant applications in control problems and partial differential equations. It provides readers the background material needed to delve deeper into the subject and explore the rich research literature. In addition, the book addresses many of the basic techniques and results recently developed in connection with this theory, including the structure of solution sets for evolution inclusions with m-dissipative operators; quasi-autonomous and non-autonomous evolution inclusions and control systems; evolution inclusions with the Hille-Yosida operator; functional evolution inclusions; impulsive evolution inclusions; and stochastic evolution inclusions. Several applications of evolution inclusions and control systems are also discussed in detail. Based on extensive research work conducted by the authors and other experts over the past four years, the information presented is cutting-edge and comprehensive. As such, the book fills an important gap in the body of literature on the structure of evolution inclusions and its applications.
Wavelet Numerical Method and Its Applications in Nonlinear Problems (Engineering Applications of Computational Methods #6)
by You-He ZhouThis book summarizes the basic theory of wavelets and some related algorithms in an easy-to-understand language from the perspective of an engineer rather than a mathematician. In this book, the wavelet solution schemes are systematically established and introduced for solving general linear and nonlinear initial boundary value problems in engineering, including the technique of boundary extension in approximating interval-bounded functions, the calculation method for various connection coefficients, the single-point Gaussian integration method in calculating the coefficients of wavelet expansions and unique treatments on nonlinear terms in differential equations. At the same time, this book is supplemented by a large number of numerical examples to specifically explain procedures and characteristics of the method, as well as detailed treatments for specific problems. Different from most of the current monographs focusing on the basic theory of wavelets, it focuses on the use of wavelet-based numerical methods developed by the author over the years. Even for the necessary basic theory of wavelet in engineering applications, this book is based on the author’s own understanding in plain language, instead of a relatively difficult professional mathematical description. This book is very suitable for students, researchers and technical personnel who only want to need the minimal knowledge of wavelet method to solve specific problems in engineering.
Software Adaptation in an Open Environment: A Software Architecture Perspective
by Yu Zhou Taolue ChenThe book is about a very active research field in software engineering. In modern society, the fact of the world's high reliance on software requires the system's robustness, i.e., continual availability and satisfactory service quality. This requirement gives rise to the popularity of the research on the self-adaptive software in open environment. There are some academic conferences dedicated to this field. But there is a lack of monographs about the topic. We believe such need is unmet in marketplace. By publishing the book, it can help bridge the gap and bring benefits to readers thereof. Key Features: The topic is well-motivated, interesting and actively studied worldwide The research represents as the state-of-the-art in the field The technical part of the book is rigidly evaluated The theoretical part of the book is sound and proved The organization and presentation of the book will be double-checked by professional scholars
Software Adaptation in an Open Environment: A Software Architecture Perspective
by Yu Zhou Taolue ChenThe book is about a very active research field in software engineering. In modern society, the fact of the world's high reliance on software requires the system's robustness, i.e., continual availability and satisfactory service quality. This requirement gives rise to the popularity of the research on the self-adaptive software in open environment. There are some academic conferences dedicated to this field. But there is a lack of monographs about the topic. We believe such need is unmet in marketplace. By publishing the book, it can help bridge the gap and bring benefits to readers thereof. Key Features: The topic is well-motivated, interesting and actively studied worldwide The research represents as the state-of-the-art in the field The technical part of the book is rigidly evaluated The theoretical part of the book is sound and proved The organization and presentation of the book will be double-checked by professional scholars
Ensemble Methods: Foundations and Algorithms
by Zhi-Hua ZhouAn up-to-date, self-contained introduction to a state-of-the-art machine learning approach, Ensemble Methods: Foundations and Algorithms shows how these accurate methods are used in real-world tasks. It gives you the necessary groundwork to carry out further research in this evolving field.After presenting background and terminology, the book cover
Advances in Knowledge Discovery and Data Mining: 11th Pacific-Asia Conference, PAKDD 2007, Nanjing, China, May 22-25, 2007, Proceedings (Lecture Notes in Computer Science #4426)
by Zhi-Hua Zhou Hang Li Qiang YangThis book constitutes the refereed proceedings of the 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2007, held in Nanjing, China, May 2007. It covers new ideas, original research results and practical development experiences from all KDD-related areas including data mining, machine learning, data warehousing, data visualization, automatic scientific discovery, knowledge acquisition and knowledge-based systems.
The Finite Element Method: Fundamentals and Applications in Civil, Hydraulic, Mechanical and Aeronautical Engineering
by ZhuA comprehensive review of the Finite Element Method (FEM), this book provides the fundamentals together with a wide range of applications in civil, mechanical and aeronautical engineering. It addresses both the theoretical and numerical implementation aspects of the FEM, providing examples in several important topics such as solid mechanics, fluid mechanics and heat transfer, appealing to a wide range of engineering disciplines. Written by a renowned author and academician with the Chinese Academy of Engineering, The Finite Element Method would appeal to researchers looking to understand how the fundamentals of the FEM can be applied in other disciplines. Researchers and graduate students studying hydraulic, mechanical and civil engineering will find it a practical reference text.
The Finite Element Method: Fundamentals and Applications in Civil, Hydraulic, Mechanical and Aeronautical Engineering
by ZhuA comprehensive review of the Finite Element Method (FEM), this book provides the fundamentals together with a wide range of applications in civil, mechanical and aeronautical engineering. It addresses both the theoretical and numerical implementation aspects of the FEM, providing examples in several important topics such as solid mechanics, fluid mechanics and heat transfer, appealing to a wide range of engineering disciplines. Written by a renowned author and academician with the Chinese Academy of Engineering, The Finite Element Method would appeal to researchers looking to understand how the fundamentals of the FEM can be applied in other disciplines. Researchers and graduate students studying hydraulic, mechanical and civil engineering will find it a practical reference text.
Pricing and Forecasting Carbon Markets: Models and Empirical Analyses
by Bangzhu Zhu Julien ChevallierThis book applies the multidisciplinary approaches of econometrics, statistics, finance and artificial intelligence for pricing and forecasting the carbon market in the context of managerial issues. It explores the related issues of pricing and forecasting the carbon market using theoretical models and empirical analyses, demonstrating how the carbon market, as a policy-based artificial market, is complex and influenced by both the market mechanisms and the external heterogeneous environments. By integrating the features of analytical systems, it offers insights to further our scientific understanding of the pricing mechanism and the variable laws governing the carbon market. Moreover, it lays a foundation for dealing with climate change in China and constructing a national carbon market there. Ultimately, it actively contributes to the energy saving and CO2 emission reduction promoted by the carbon market.The carbon market, represented by the European Union Emissions Trading System (EU ETS), is a cost-effective measure for tackling climate change. Furthermore, pricing and forecasting carbon market has been one of the research focuses in the fields of energy and climate change. As a policy tool of the trading mechanism, the carbon market offers a great institutional innovation for coping with climate change. Due to its multiple advantages including saving costs and environment protection, and political feasibility, more and more countries including China have applied the carbon market for carbon dioxide (CO2) emission reduction. Accurately understanding the pricing mechanism and mastering the fluctuating law of carbon market is essential to build a national carbon market for China.
Representation Theory, Number Theory, and Invariant Theory: In Honor of Roger Howe on the Occasion of His 70th Birthday (Progress in Mathematics #323)
by Chen-Bo Zhu Ju-Lee Kim Jim CogdellThis book contains selected papers based on talks given at the "Representation Theory, Number Theory, and Invariant Theory" conference held at Yale University from June 1 to June 5, 2015. The meeting and this resulting volume are in honor of Professor Roger Howe, on the occasion of his 70th birthday, whose work and insights have been deeply influential in the development of these fields. The speakers who contributed to this work include Roger Howe's doctoral students, Roger Howe himself, and other world renowned mathematicians. Topics covered include automorphic forms, invariant theory, representation theory of reductive groups over local fields, and related subjects.
Infrared Small Target Detection: Theory, Methods, and Algorithms.
by Hu Zhu Yushan Pan Lizhen Deng Guoxia XuUncover the secrets of cutting-edge research in “Infrared Small Target Detection,” a crucial resource that delves into the dynamic world of infrared imaging and detection algorithms. This comprehensive book is an indispensable gem for the research community, offering a profound introduction to the theory, methods, and algorithms underlying infrared small object detection. As an invaluable guide, this book explores diverse models and categories of infrared small object detection algorithms, providing meticulous descriptions and comparisons of their strengths and limitations. Perfectly tailored for researchers, practitioners, and students with a passion for infrared imaging and detection, this book equips readers with the necessary knowledge to embark on groundbreaking investigations in this field.Readers can particularly be drawn to the book's methods, results, and topics, encompassing diverse categories of infrared small object detection algorithms and their corresponding advantages and disadvantages. The book also imparts foundational knowledge in mathematical morphology, tensor decomposition, and deep learning, enabling readers to grasp the underlying principles of these advanced algorithms. Experience the key benefits of “Infrared Small Target Detection” as readers gain a profound understanding of theory, methods, and algorithms tailored to infrared small object detection. The comprehensive descriptions and comparisons of various algorithm categories empower readers to select the perfect algorithms for their specific applications. Unlock the potential of this groundbreaking resource with a basic understanding of mathematics, statistics, and image processing. Some familiarity with infrared imaging and detection proves advantageous in fully immersing oneself in the wealth of knowledge presented within these pages.
Theory and Approaches of Unascertained Group Decision-Making (Systems Evaluation, Prediction, And Decision-making Ser.)
by Jianjun ZhuTackling the question of how to effectively aggregate uncertain preference information in multiple structures given by decision-making groups, Theory and Approaches of Unascertained Group Decision-Making focuses on group aggregation methods based on uncertainty preference information. It expresses the complexity existing in each group decision-maki
Theory and Approaches of Unascertained Group Decision-Making
by Jianjun ZhuTackling the question of how to effectively aggregate uncertain preference information in multiple structures given by decision-making groups, Theory and Approaches of Unascertained Group Decision-Making focuses on group aggregation methods based on uncertainty preference information. It expresses the complexity existing in each group decision-maki
Applications of Fourier Transform to Smile Modeling: Theory and Implementation (Springer Finance)
by Jianwei ZhuThis book addresses the applications of Fourier transform to smile modeling. Smile effect is used generically by ?nancial engineers and risk managers to refer to the inconsistences of quoted implied volatilities in ?nancial markets, or more mat- matically, to the leptokurtic distributions of ?nancial assets and indices. Therefore, a sound modeling of smile effect is the central challenge in quantitative ?nance. Since more than one decade, Fourier transform has triggered a technical revolution in option pricing theory. Almost all new developed option pricing models, es- cially in connection with stochastic volatility and random jump, have extensively applied Fourier transform and the corresponding inverse transform to express - tion pricing formulas. The large accommodation of the Fourier transform allows for a very convenient modeling with a general class of stochastic processes and d- tributions. This book is then intended to present a comprehensive treatment of the Fourier transform in the option valuation, covering the most stochastic factors such as stochastic volatilities and interest rates, Poisson and Levy ´ jumps, including some asset classes such as equity, FX and interest rates, and providing numerical ex- ples and prototype programming codes. I hope that readers will bene?t from this book not only by gaining an overview of the advanced theory and the vast large l- erature on these topics, but also by gaining a ?rst-hand feedback from the practice on the applications and implementations of the theory.
Modular Pricing of Options: An Application of Fourier Analysis (Lecture Notes in Economics and Mathematical Systems #493)
by Jianwei ZhuFrom a technical point of view, the celebrated Black and Scholes option pricing formula was originally developed using a separation of variables technique. However, already Merton mentioned in his seminal 1973 pa per, that it could have been developed by using Fourier transforms as well. Indeed, as is well known nowadays, Fourier transforms are a rather convenient solution technique for many models involving the fundamental partial differential equation of financial economics. It took the community nearly another twenty years to recognize that Fourier transform is even more useful, if one applies it to problems in financial economics without seeking an explicit analytical inverse trans form. Heston (1993) probably was the first to demonstrate how to solve a stochastic volatility option pricing model quasi analytically using the characteristic function of the problem, which is nothing else than the Fourier transform of the underlying Arrow /Debreu-prices, and doing the inverse transformation numerically. This opened the door for a whole bunch of new closed form solutions in the transformed Fourier space and still is one of the most active research areas in financial economics.
Bogoliubov-de Gennes Method and Its Applications (Lecture Notes in Physics #924)
by Jian-Xin ZhuThe purpose of this book is to provide an elementary yet systematic description of the Bogoliubov-de Gennes (BdG) equations, their unique symmetry properties and their relation to Green’s function theory. Specifically, it introduces readers to the supercell technique for the solutions of the BdG equations, as well as other related techniques for more rapidly solving the equations in practical applications.The BdG equations are derived from a microscopic model Hamiltonian with an effective pairing interaction and fully capture the local electronic structure through self-consistent solutions via exact diagonalization. This approach has been successfully generalized to study many aspects of conventional and unconventional superconductors with inhomogeneities – including defects, disorder or the presence of a magnetic field – and becomes an even more attractive choice when the first-principles information of a typical superconductor is incorporated via the construction of a low-energy tight-binding model. Further, the lattice BdG approach is essential when theoretical results for local electronic states around such defects are compared with the scanning tunneling microscopy measurements.Altogether, these lectures provide a timely primer for graduate students and non-specialist researchers, while also offering a useful reference guide for experts in the field.
Limits of Stability and Stabilization of Time-Delay Systems: A Small-Gain Approach (Advances in Delays and Dynamics #8)
by Jing Zhu Tian Qi Dan Ma Jie ChenThis authored monograph presents a study on fundamental limits and robustness of stability and stabilization of time-delay systems, with an emphasis on time-varying delay, robust stabilization, and newly emerged areas such as networked control and multi-agent systems. The authors systematically develop an operator-theoretic approach that departs from both the traditional algebraic approach and the currently pervasive LMI solution methods. This approach is built on the classical small-gain theorem, which enables the author to draw upon powerful tools and techniques from robust control theory. The book contains motivating examples and presents mathematical key facts that are required in the subsequent sections. The target audience primarily comprises researchers and professionals in the field of control theory, but the book may also be beneficial for graduate students alike.
Quantitative Models for Performance Evaluation and Benchmarking: Data Envelopment Analysis with Spreadsheets (International Series in Operations Research & Management Science #51)
by Joe ZhuManagers are often under great pressure to improve the performance of their organizations. To improve performance, one needs to constantly evaluate operations or processes related to producing products, providing services, and marketing and selling products. Performance evaluation and benchmarking are a widely used method to identify and adopt best practices as a means to improve performance and increase productivity, and are particularly valuable when no objective or engineered standard is available to define efficient and effective performance. For this reason, benchmarking is often used in managing service operations, because service standards (benchmarks) are more difficult to define than manufacturing standards. Benchmarks can be established but they are somewhat limited as they work with single measurements one at a time. It is difficult to evaluate an organization's performance when there are multiple inputs and outputs to the system. The difficulties are further enhanced when the relationships between the inputs and the outputs are complex and involve unknown tradeoffs. It is critical to show benchmarks where multiple measurements exist. The current book introduces the methodology of data envelopment analysis (DEA) and its uses in performance evaluation and benchmarking under the context of mUltiple performance measures.
Quantitative Models for Performance Evaluation and Benchmarking: Data Envelopment Analysis with Spreadsheets (International Series in Operations Research & Management Science #126)
by Joe ZhuManagers are often under great pressure to improve the performance of their organizations. To improve performance, one needs to constantly evaluate operations or processes related to producing products, providing services, and marketing and selling products. Performance evaluation and benchmarking are a widely used method to identify and adopt best practices as a means to improve performance and increase productivity, and are particularly valuable when no objective or engineered standard is available to define efficient and effective performance. For this reason, benchmarking is often used in managing service operations, because service standards (benchmarks) are more difficult to define than manufacturing standards. Benchmarks can be established but they are somewhat limited as they work with single measurements one at a time. It is difficult to evaluate an organization’s performance when there are multiple inputs and outputs to the system. The difficulties are further enhanced when the relationships between the inputs and the outputs are complex and involve unknown tradeoffs. It is critical to show benchmarks where multiple measurements exist. The current book introduces the methodology of data envelopment analysis (DEA) and its uses in performance evaluation and benchmarking under the context of multiple performance measures.