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This book presents statistical methods and models of importance to quantitative finance and links finance theory to market practice via statistical modeling and decision making. Part I provides basic background in statistics, which includes linear regression and extensions to generalized linear models and nonlinear regression, multivariate analysis, likelihood inference and Bayesian methods, and time series analysis. It also describes applications of these methods to portfolio theory and dynamic models of asset returns and their volatilities. Part II presents advanced topics in quantitative finance and introduces a substantive-empirical modeling approach to address the discrepancy between finance theory and market data. It describes applications to option pricing, interest rate markets, statistical trading strategies, and risk management. Nonparametric regression, advanced multivariate and time series methods in financial econometrics, and statistical models for high-frequency transactions data are also introduced in this connection. The book has been developed as a textbook for courses on statistical modeling in quantitative finance in master's level financial mathematics (or engineering) and computational (or mathematical) finance programs. It is also designed for self-study by quantitative analysts in the financial industry who want to learn more about the background and details of the statistical methods used by the industry. It can also be used as a reference for graduate statistics and econometrics courses on regression, multivariate analysis, likelihood and Bayesian inference, nonparametrics, and time series, providing concrete examples and data from financial markets to illustrate the statistical methods. Tze Leung Lai is Professor of Statistics and Director of Financial Mathematics at Stanford University. He received the Ph.D. degree in 1971 from Columbia University, where he remained on the faculty until moving to Stanford University in 1987. He received the Committee of Presidents of Statistical Societies Award in 1983 and is an elected member of Academia Sinica and the International Statistical Institute. His research interests include quantitative finance and risk management, sequential statistical methodology, stochastic optimization and adaptive control, probability theory and stochastic processes, econometrics, and biostatistics. Haipeng Xing is Assistant Professor of Statistics at Columbia University. He received the Ph.D. degree in 2005 from Stanford University. His research interests include financial econometrics and engineering, time series modeling and adaptive control, fault detection, and change-point problems.

Finance --- Mathematical models. --- Statistical methods. --- Public finance. --- Distribution (Probability theory. --- Statistics. --- Finance. --- Public Economics. --- Probability Theory and Stochastic Processes. --- Statistics for Business, Management, Economics, Finance, Insurance. --- Quantitative Finance. --- Funding --- Funds --- Economics --- Currency question --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Distribution functions --- Frequency distribution --- Characteristic functions --- Probabilities --- Cameralistics --- Public finance --- Public finances --- Probabilities. --- Statistics . --- Economics, Mathematical . --- Mathematical economics --- Probability --- Statistical inference --- Combinations --- Chance --- Least squares --- Mathematical statistics --- Risk --- Methodology

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This book presents an integrated methodology for sequential experimentation in clinical trials. The methodology allows sequential learning during the course of a trial to improve the efficiency of the trial design, which often lacks adequate information at the planning stage. Adaptation via sequential learning of unknown parameters is a central idea not only in adaptive designs of confirmatory clinical trials but also in the theory of optimal nonlinear experimental design, which the book covers as introductory material. Other introductory topics for which the book provides preparatory background include sequential testing theory, dynamic programming and stochastic optimization, survival analysis and resampling methods. In this way, the book gives a self-contained and thorough treatment of group sequential and adaptive designs, time-sequential trials with failure-time endpoints, and statistical inference at the conclusion of these trials. The book can be used for graduate courses in sequential analysis, clinical trials, and biostatistics, and also for short courses on clinical trials at professional meetings. Each chapter ends with supplements for the reader to explore related concepts and methods, and problems which can be used for exercises in graduate courses. Jay Bartroff is Associate Professor of Mathematics at the University of Southern California where he is a member of the Laboratory of Applied Pharmacokinetics at the USC Keck School of Medicine. He is a leading expert on group sequential and multistage adaptive statistical procedures and their applications to clinical trial designs, and he is a sought-after consultant in academia and industry. Tze Leung Lai is Professor of Statistics, and by courtesy, of Health Research and Policy and of the Institute of Computational and Mathematical Engineering at Stanford University, where he is the Director of the Financial and Risk Modeling Institute and Co-director of the Biostatistics Core at the Stanford Cancer Institute and of the Center for Innovative Study Design at the School of Medicine. He made seminal contributions to sequential analysis, innovative clinical trial designs, adaptive methods, survival analysis, nonlinear and generalized mixed models, hybrid resampling methods, and received the Committee of Presidents of Statistical Societies (COPSS) Award in 1983. Mei-Chiung Shih is Assistant Professor of Biostatistics and a member of the Stanford Cancer Institute and of the Center for Innovative Study Design at the School of Medicine at Stanford University. She is also Associate Director for Scientific and Technical Operations at the Department of Veterans Affairs (VA) Cooperative Studies Program Coordinating Center at Palo Alto Health Care System. She is a leading expert on group sequential and adaptive designs and inference of clinical trials, longitudinal and survival data analysis, and has been leading the design, conduct and analysis of several large trials at the VA.

Clinical trials -- Statistical methods. --- Clinical trials. --- Drugs -- Testing -- Statistical methods. --- Experimental design. --- Clinical trials --- Sequential analysis --- Epidemiologic Study Characteristics as Topic --- Health Care Evaluation Mechanisms --- Epidemiologic Methods --- Epidemiologic Research Design --- Investigative Techniques --- Models, Theoretical --- Research Design --- Mathematics --- Evaluation Studies as Topic --- Quality of Health Care --- Analytical, Diagnostic and Therapeutic Techniques and Equipment --- Public Health --- Natural Science Disciplines --- Disciplines and Occupations --- Environment and Public Health --- Health Care Quality, Access, and Evaluation --- Health Care --- Models, Statistical --- Statistics as Topic --- Methods --- Clinical Trials as Topic --- Meta-Analysis as Topic --- Medicine --- Health & Biological Sciences --- Physical Sciences & Mathematics --- Medical Research --- Mathematical Statistics --- Statistical methods --- Statistical methods. --- Mathematical Sciences --- Applied Mathematics --- Statistics. --- Statistics for Life Sciences, Medicine, Health Sciences. --- Statistics, general. --- Statistical Theory and Methods. --- Mathematical statistics. --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Statistical analysis --- Statistical data --- Statistical science --- Econometrics --- Statistics .

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This book presents statistical methods and models of importance to quantitative finance and links finance theory to market practice via statistical modeling and decision making. Part I provides basic background in statistics, which includes linear regression and extensions to generalized linear models and nonlinear regression, multivariate analysis, likelihood inference and Bayesian methods, and time series analysis. It also describes applications of these methods to portfolio theory and dynamic models of asset returns and their volatilities. Part II presents advanced topics in quantitative finance and introduces a substantive-empirical modeling approach to address the discrepancy between finance theory and market data. It describes applications to option pricing, interest rate markets, statistical trading strategies, and risk management. Nonparametric regression, advanced multivariate and time series methods in financial econometrics, and statistical models for high-frequency transactions data are also introduced in this connection. The book has been developed as a textbook for courses on statistical modeling in quantitative finance in master's level financial mathematics (or engineering) and computational (or mathematical) finance programs. It is also designed for self-study by quantitative analysts in the financial industry who want to learn more about the background and details of the statistical methods used by the industry. It can also be used as a reference for graduate statistics and econometrics courses on regression, multivariate analysis, likelihood and Bayesian inference, nonparametrics, and time series, providing concrete examples and data from financial markets to illustrate the statistical methods. Tze Leung Lai is Professor of Statistics and Director of Financial Mathematics at Stanford University. He received the Ph.D. degree in 1971 from Columbia University, where he remained on the faculty until moving to Stanford University in 1987. He received the Committee of Presidents of Statistical Societies Award in 1983 and is an elected member of Academia Sinica and the International Statistical Institute. His research interests include quantitative finance and risk management, sequential statistical methodology, stochastic optimization and adaptive control, probability theory and stochastic processes, econometrics, and biostatistics. Haipeng Xing is Assistant Professor of Statistics at Columbia University. He received the Ph.D. degree in 2005 from Stanford University. His research interests include financial econometrics and engineering, time series modeling and adaptive control, fault detection, and change-point problems.

Quantitative methods (economics) --- Mathematical statistics --- Financial analysis --- Business economics --- statistiek --- financiële analyse --- econometrie

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Self-normalized processes are of common occurrence in probabilistic and statistical studies. A prototypical example is Student's t-statistic introduced in 1908 by Gosset, whose portrait is on the front cover. Due to the highly non-linear nature of these processes, the theory experienced a long period of slow development. In recent years there have been a number of important advances in the theory and applications of self-normalized processes. Some of these developments are closely linked to the study of central limit theorems, which imply that self-normalized processes are approximate pivots for statistical inference. The present volume covers recent developments in the area, including self-normalized large and moderate deviations, and laws of the iterated logarithms for self-normalized martingales. This is the first book that systematically treats the theory and applications of self-normalization.

Statistical science --- Operational research. Game theory --- stochastische analyse --- kansrekening --- statistisch onderzoek

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This book presents the proceedings of the 2nd Pacific Rim Statistical Conference for Production Engineering: Production Engineering, Big Data and Statistics, which took place at Seoul National University in Seoul, Korea in December, 2016. The papers included discuss a wide range of statistical challenges, methods and applications for big data in production engineering, and introduce recent advances in relevant statistical methods.

Production engineering --- Big data --- Statistical methods --- Statistics. --- Big data. --- Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. --- Big Data/Analytics. --- Data sets, Large --- Large data sets --- Statistical analysis --- Statistical data --- Statistical science --- Mathematics --- Econometrics --- Manufacturing engineering --- Process engineering --- Industrial engineering --- Mechanical engineering --- Data sets --- Statistics .