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Book
Simulation and Inference for Stochastic Differential Equations
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ISBN: 9780387758381 9780387758398 0387758380 9786612237683 1282237683 0387758399 Year: 2008 Publisher: New York, NY Springer-Verlag New York

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Abstract

This book is unique because of its focus on the practical implementation of the simulation and estimation methods presented. The book will be useful to practitioners and students with only a minimal mathematical background because of the many R programs, and to more mathematically-educated practitioners. Many of the methods presented in the book have not been used much in practice because the lack of an implementation in a unified framework. This book fills the gap. With the R code included in this book, a lot of useful methods become easy to use for practitioners and students. An R package called "sde" provides functions with easy interfaces ready to be used on empirical data from real life applications. Although it contains a wide range of results, the book has an introductory character and necessarily does not cover the whole spectrum of simulation and inference for general stochastic differential equations. The book is organized into four chapters. The first one introduces the subject and presents several classes of processes used in many fields of mathematics, computational biology, finance and the social sciences. The second chapter is devoted to simulation schemes and covers new methods not available in other publications. The third one focuses on parametric estimation techniques. In particular, it includes exact likelihood inference, approximated and pseudo-likelihood methods, estimating functions, generalized method of moments, and other techniques. The last chapter contains miscellaneous topics like nonparametric estimation, model identification and change point estimation. The reader who is not an expert in the R language will find a concise introduction to this environment focused on the subject of the book. A documentation page is available at the end of the book for each R function presented in the book. Stefano M. Iacus is associate professor of Probability and Mathematical Statistics at the University of Milan, Department of Economics, Business and Statistics. He has a PhD in Statistics at Padua University, Italy and in Mathematics at Université du Maine, France. He is a member of the R Core team for the development of the R statistical environment, Data Base manager for the Current Index to Statistics, and IMS Group Manager for the Institute of Mathematical Statistics. He has been associate editor of the Journal of Statistical Software.

Keywords

Stochastic processes --- Statistics. --- Signal, Image and Speech Processing. --- Computational Mathematics and Numerical Analysis. --- Quantitative Finance. --- Simulation and Modeling. --- Econometrics. --- Statistics and Computing/Statistics Programs. --- Computer simulation. --- Finance. --- Computer science --- Mathematical statistics. --- Statistique --- Simulation par ordinateur --- Finances --- Informatique --- Statistique mathématique --- Econométrie --- Mathematics. --- Mathématiques --- 517.96 --- 519.216 --- Finite differences. Functional and integral equations --- Stochastic processes in general. Prediction theory. Stopping times. Martingales --- Equations. --- Stochastic differential equations. --- Stochastic differential equations --- Mathematics --- Physical Sciences & Mathematics --- Mathematical Statistics --- 519.216 Stochastic processes in general. Prediction theory. Stopping times. Martingales --- 517.96 Finite differences. Functional and integral equations --- Ergodic theory. --- Ergodic transformations --- Mathematical analysis. --- Analysis (Mathematics). --- Economics, Mathematical. --- Probabilities. --- Probability Theory and Stochastic Processes. --- Analysis. --- Differential equations --- Fokker-Planck equation --- Continuous groups --- Mathematical physics --- Measure theory --- Transformations (Mathematics) --- Distribution (Probability theory. --- Global analysis (Mathematics). --- Computer modeling --- Computer models --- Modeling, Computer --- Models, Computer --- Simulation, Computer --- Electromechanical analogies --- Mathematical models --- Simulation methods --- Model-integrated computing --- Economics, Mathematical --- Statistics --- Funding --- Funds --- Economics --- Currency question --- Analysis, Global (Mathematics) --- Differential topology --- Functions of complex variables --- Geometry, Algebraic --- Distribution functions --- Frequency distribution --- Characteristic functions --- Probabilities --- Statistical inference --- Statistics, Mathematical --- Sampling (Statistics) --- Statistical methods --- Statistics . --- Economics, Mathematical . --- Mathematical economics --- Econometrics --- 517.1 Mathematical analysis --- Mathematical analysis --- Probability --- Combinations --- Chance --- Least squares --- Mathematical statistics --- Risk --- Statistical analysis --- Statistical data --- Statistical science --- Methodology --- R (Computer program language). --- GNU-S (Computer program language) --- Domain-specific programming languages --- Social sciences --- Statistics and Computing. --- Probability Theory. --- Mathematics in Business, Economics and Finance. --- Computer Modelling. --- Data processing.

Microfluidics for biological applications
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Microfluidics for Biological Applications provides researchers and scientists in the biotechnology, pharmaceutical, and life science industries with an introduction to the basics of microfluidics and discusses how to link these technologies to various biological applications at the industrial and academic level. Readers will gain insight into a wide variety of biological applications for microfluidics. The book begins with a perspective on the history and development of microfluidic technologies, followed by an overview on how microfluidic systems have been used to study and manipulate specific classes of components, including a discussion of specific biological applications of microfluidics and concludes with a discussion of emerging trends in the field. Microfluidics for Biological Applications provides information about the latest techniques and trends including: Fabrication methods for microfluidic devices, including those using biodegradable materials Use of microfluidics for high throughput screening Microfluidic methods for detection of pathogens in diagnostic and biodefense applications Analysis and manipulation of DNA, proteins, and cells in a microfluidic device Use of microfluidic platforms for tissue engineering and vascularization Microfluidics for Biological Applications is an ideal reference for researchers and practicing engineers, as well as graduate students who are either entering the field for the first time, or those already conducting research and who want to expand their knowledge in the area of microfluidics.

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