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A First Course in Statistics for Signal Analysis / Wojbor Andrzej Woyczynski / Bâle (CHE) ; Boston, MA : Birkhäuser (2019)

Titre : A First Course in Statistics for Signal Analysis Type de document : document électronique Auteurs : Wojbor Andrzej Woyczynski (1943-....), ; SpringerLink (Online service) Editeur : Bâle (CHE) ; Boston, MA : Birkhäuser Année de publication : 2019 Collection : Statistics for Industry, Technology, and Engineering, ISSN 2662-5555 Importance : XVIII, 332 p. 95 illus., 69 illus. in color Présentation : online resource ISBN/ISSN/EAN : 978-3-030-20908-7 Langues : Anglais ( eng)Tags : Statistics Fourier analysis Signal processing Image processing Speech processing systems Statistics for Engineering Physics Computer Science Chemistry and Earth Sciences Fourier Analysis Signal Image and Speech Processing Résumé : This essentially self-contained, deliberately compact, and user-friendly textbook is designed for a first, one-semester course in statistical signal analysis for a broad audience of students in engineering and the physical sciences. The emphasis throughout is on fundamental concepts and relationships in the statistical theory of stationary random signals, explained in a concise, yet fairly rigorous presentation. Topics and Features: · Fourier series and transforms—fundamentally important in random signal analysis and processing—are developed from scratch, emphasizing the time-domain vs. frequency-domain duality; · Basic concepts of probability theory, laws of large numbers, the central limit theorem, and statistical parametric inference procedures are presented so that no prior knowledge of probability and statistics is required; the only prerequisite is a basic two–three semester calculus sequence; · Computer simulation algorithms of stationary random signals with a given power spectrum density; · Complementary bibliography for readers who wish to pursue the study of random signals in greater depth; · Many diverse examples and end-of-chapter problems and exercises. Developed by the author over the course of many years of classroom use, A First Course in Statistics for Signal Analysis, Second Edition may be used by junior/senior undergraduates or graduate students in electrical, systems, computer, and biomedical engineering, as well as the physical sciences. The work is also an excellent resource of educational and training material for scientists and engineers working in research laboratories. This third edition contains two additional chapters that present wavelets and the uncertainty principle, and the forecasting problems for stationary time series. These two topics are essential for students to attain a deeper understanding of statistical analysis of random signals. Reviews from previous editions: A First Course in Statistics for Signal Analysis is a small, dense, and inexpensive book that covers exactly what the title says: statistics for signal analysis. The book has much to recommend it. The author clearly understands the topics presented. The topics are covered in a rigorous manner, but not so rigorous as to be ostentatious. JASA (Review of the First Edition) This is a nicely written self-contained book and it is a good candidate for adoption as a textbook for upper-level undergraduate and even for a graduate course for engineering and physical sciences students. … I have no hesitation in recommending it as a textbook for the targeted course and audience. Technometrics, Vol. 53 (4), November, 2011 (Review of the Second Edition) Note de contenu : Description of Signals -- Spectral Representation of Deterministic Signals: Fourier Series and Transforms -- Uncertainty Principle and Wavelet Transforms -- Random Variables and Random Vectors -- Stationary Signals -- Power Spectra of Random Signals -- Transmission of Stationary Signals through Linear Systems -- Optimization of Signal-to-Noise Ratio in Linear Systems -- Gaussian Signals, Covariance Matrices, and Sample Path Properties -- Spectral Representation of Discrete-Time Signals and Their Computer Simulations -- Prediction Theory for Stationary Random Signals -- Solutions to Selected Problems and Exercises Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=148208 A First Course in Statistics for Signal Analysis [document électronique] / Wojbor Andrzej Woyczynski (1943-....), ; SpringerLink (Online service) . - Bâle (CHE) ; Boston, MA : Birkhäuser, 2019 . - XVIII, 332 p. 95 illus., 69 illus. in color : online resource. - (Statistics for Industry, Technology, and Engineering, ISSN 2662-5555) .ISBN: 978-3-030-20908-7

Langues : Anglais (eng)

Tags : Statistics Fourier analysis Signal processing Image processing Speech processing systems Statistics for Engineering Physics Computer Science Chemistry and Earth Sciences Fourier Analysis Signal Image and Speech Processing Résumé : This essentially self-contained, deliberately compact, and user-friendly textbook is designed for a first, one-semester course in statistical signal analysis for a broad audience of students in engineering and the physical sciences. The emphasis throughout is on fundamental concepts and relationships in the statistical theory of stationary random signals, explained in a concise, yet fairly rigorous presentation. Topics and Features: · Fourier series and transforms—fundamentally important in random signal analysis and processing—are developed from scratch, emphasizing the time-domain vs. frequency-domain duality; · Basic concepts of probability theory, laws of large numbers, the central limit theorem, and statistical parametric inference procedures are presented so that no prior knowledge of probability and statistics is required; the only prerequisite is a basic two–three semester calculus sequence; · Computer simulation algorithms of stationary random signals with a given power spectrum density; · Complementary bibliography for readers who wish to pursue the study of random signals in greater depth; · Many diverse examples and end-of-chapter problems and exercises. Developed by the author over the course of many years of classroom use, A First Course in Statistics for Signal Analysis, Second Edition may be used by junior/senior undergraduates or graduate students in electrical, systems, computer, and biomedical engineering, as well as the physical sciences. The work is also an excellent resource of educational and training material for scientists and engineers working in research laboratories. This third edition contains two additional chapters that present wavelets and the uncertainty principle, and the forecasting problems for stationary time series. These two topics are essential for students to attain a deeper understanding of statistical analysis of random signals. Reviews from previous editions: A First Course in Statistics for Signal Analysis is a small, dense, and inexpensive book that covers exactly what the title says: statistics for signal analysis. The book has much to recommend it. The author clearly understands the topics presented. The topics are covered in a rigorous manner, but not so rigorous as to be ostentatious. JASA (Review of the First Edition) This is a nicely written self-contained book and it is a good candidate for adoption as a textbook for upper-level undergraduate and even for a graduate course for engineering and physical sciences students. … I have no hesitation in recommending it as a textbook for the targeted course and audience. Technometrics, Vol. 53 (4), November, 2011 (Review of the Second Edition) Note de contenu : Description of Signals -- Spectral Representation of Deterministic Signals: Fourier Series and Transforms -- Uncertainty Principle and Wavelet Transforms -- Random Variables and Random Vectors -- Stationary Signals -- Power Spectra of Random Signals -- Transmission of Stationary Signals through Linear Systems -- Optimization of Signal-to-Noise Ratio in Linear Systems -- Gaussian Signals, Covariance Matrices, and Sample Path Properties -- Spectral Representation of Discrete-Time Signals and Their Computer Simulations -- Prediction Theory for Stationary Random Signals -- Solutions to Selected Problems and Exercises Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=148208 Contributions on Theory of Mathematical Statistics / Kei Takeuchi / Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer (2020)

Titre : Contributions on Theory of Mathematical Statistics Type de document : document électronique Auteurs : Kei Takeuchi, ; SpringerLink (Online service) Editeur : Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer Année de publication : 2020 Importance : XI, 435 p. 17 illus Présentation : online resource ISBN/ISSN/EAN : 978-4-431-55239-0 Langues : Anglais ( eng)Tags : Statistics Statistical Theory and Methods Statistics for Business Management Economics Finance Insurance Statistics for Social Sciences Humanities Law Résumé : This volume is a reorganized edition of Kei Takeuchi’s works on various problems in mathematical statistics based on papers and monographs written since the 1960s on several topics in mathematical statistics and published in various journals in English and in Japanese. They are organized into seven parts, each of which is concerned with specific topics and edited to make a consistent thesis. Sometimes expository chapters have been added. The topics included are as follows: theory of statistical prediction from a non-Bayesian viewpoint and analogous to the classical theory of statistical inference; theory of robust estimation, concepts, and procedures, and its implications for practical applications; theory of location and scale covariant/invariant estimations with derivation of explicit forms in various cases; theory of selection and testing of parametric models and a comprehensive approach including the derivation of the Akaike’s Information Criterion (AIC); theory of randomized designs, comparisons of random and conditional approaches, and of randomized and non-randomized designs, with random sampling from finite populations considered as a special case of randomized designs and with some separate independent papers included. Theory of asymptotically optimal and higher-order optimal estimators are not included, since most of them already have been published in the Joint Collected Papers of M. Akahira and K. Takeuchi. There are some topics that are not necessarily new, do not seem to have attracted many theoretical statisticians, and do not appear to have been systematically dealt with in textbooks or expository monographs. One purpose of this volume is to give a comprehensive view of such problems as well Note de contenu : Part I Statistical Prediction -- 1 Theory of Statistical Prediction -- Part II Unbiased Estimation -- 2 Unbiased Estimation in Case of the Class of Distributions of Finite Rank -- 3 Some Theorems on Invariant Estimators of Location -- Part III Robust Estimation -- 4 Robust Estimation and Robust Parameter -- 5 Robust Estimation of Location in the Case of Measurement of Physical Quantity -- 6 A Uniformly Asymptotically Efficient Estimator of a Location Parameter -- Part IV Randomization -- 7 Theory of Randomized Designs -- 8 Some Remarks on General Theory for Unbiased Estimation of a Real Parameter of a Finite Population -- Part V Tests of Normality -- 9 The Studentized Empirical Characteristic Function and Its Application to Test for the Shape of Distribution -- 10 Tests of Univariate Normality -- 11 The Tests for Multivariate Normality -- Part VI Model Selection -- 12 On the Problem of Model Selection Based on the Data.-Part VII Asymptotic Approximation -- 13 On Sum of 0-1 Random Variables (I. Univariate Case) -- 14 On Sum of 0-1 Random Variables (II. Multivariate Case) -- 15 Algebraic Properties and Validity of Univariate and Multivariate Cornish-Fisher Expansion Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=149747 Contributions on Theory of Mathematical Statistics [document électronique] / Kei Takeuchi, ; SpringerLink (Online service) . - Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer, 2020 . - XI, 435 p. 17 illus : online resource.ISBN: 978-4-431-55239-0

Langues : Anglais (eng)

Tags : Statistics Statistical Theory and Methods Statistics for Business Management Economics Finance Insurance Statistics for Social Sciences Humanities Law Résumé : This volume is a reorganized edition of Kei Takeuchi’s works on various problems in mathematical statistics based on papers and monographs written since the 1960s on several topics in mathematical statistics and published in various journals in English and in Japanese. They are organized into seven parts, each of which is concerned with specific topics and edited to make a consistent thesis. Sometimes expository chapters have been added. The topics included are as follows: theory of statistical prediction from a non-Bayesian viewpoint and analogous to the classical theory of statistical inference; theory of robust estimation, concepts, and procedures, and its implications for practical applications; theory of location and scale covariant/invariant estimations with derivation of explicit forms in various cases; theory of selection and testing of parametric models and a comprehensive approach including the derivation of the Akaike’s Information Criterion (AIC); theory of randomized designs, comparisons of random and conditional approaches, and of randomized and non-randomized designs, with random sampling from finite populations considered as a special case of randomized designs and with some separate independent papers included. Theory of asymptotically optimal and higher-order optimal estimators are not included, since most of them already have been published in the Joint Collected Papers of M. Akahira and K. Takeuchi. There are some topics that are not necessarily new, do not seem to have attracted many theoretical statisticians, and do not appear to have been systematically dealt with in textbooks or expository monographs. One purpose of this volume is to give a comprehensive view of such problems as well Note de contenu : Part I Statistical Prediction -- 1 Theory of Statistical Prediction -- Part II Unbiased Estimation -- 2 Unbiased Estimation in Case of the Class of Distributions of Finite Rank -- 3 Some Theorems on Invariant Estimators of Location -- Part III Robust Estimation -- 4 Robust Estimation and Robust Parameter -- 5 Robust Estimation of Location in the Case of Measurement of Physical Quantity -- 6 A Uniformly Asymptotically Efficient Estimator of a Location Parameter -- Part IV Randomization -- 7 Theory of Randomized Designs -- 8 Some Remarks on General Theory for Unbiased Estimation of a Real Parameter of a Finite Population -- Part V Tests of Normality -- 9 The Studentized Empirical Characteristic Function and Its Application to Test for the Shape of Distribution -- 10 Tests of Univariate Normality -- 11 The Tests for Multivariate Normality -- Part VI Model Selection -- 12 On the Problem of Model Selection Based on the Data.-Part VII Asymptotic Approximation -- 13 On Sum of 0-1 Random Variables (I. Univariate Case) -- 14 On Sum of 0-1 Random Variables (II. Multivariate Case) -- 15 Algebraic Properties and Validity of Univariate and Multivariate Cornish-Fisher Expansion Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=149747 High-Frequency Statistics with Asynchronous and Irregular Data / Martin, Ole / Heidelberg (DEU) ; Wiesbaden (DEU) : Springer Spektrum (2019)

Titre : High-Frequency Statistics with Asynchronous and Irregular Data Type de document : document électronique Auteurs : Martin, Ole, ; SpringerLink (Online service) Editeur : Heidelberg (DEU) ; Wiesbaden (DEU) : Springer Spektrum Année de publication : 2019 Collection : Mathematische Optimierung und Wirtschaftsmathematik = Mathematical Optimization and Economathematics, ISSN 2523-7926 Importance : XIII, 323 p. 34 illus Présentation : online resource ISBN/ISSN/EAN : 978-3-658-28418-3 Langues : Anglais ( eng)Tags : Probabilities Statistics Economics Mathematical Probability Theory and Stochastic Processes Statistics for Business Management Finance Insurance Quantitative Finance Résumé : Ole Martin extends well-established techniques for the analysis of high-frequency data based on regular observations to the more general setting of asynchronous and irregular observations. Such methods are much needed in practice as real data usually comes in irregular form. In the theoretical part he develops laws of large numbers and central limit theorems as well as a new bootstrap procedure to assess asymptotic laws. The author then applies the theoretical results to estimate the quadratic covariation and to construct tests for the presence of common jumps. The simulation results show that in finite samples his methods despite the much more complex setting perform comparably well as methods based on regular data. Contents Laws of Large Numbers Random Observation Schemes Bootstrapping Asymptotic Laws Testing for (Common) Jumps Target Groups Scientists and students in the field of mathematical statistics, econometrics and financial mathematics Practitioners in the field of financial mathematics About the Author Dr. Ole Martin completed his PhD at the Kiel University (CAU), Germany. His research focuses on high-frequency statistics for semimartingales with the aim to develop methods based on irregularly observed data Note de contenu : Laws of Large Numbers -- Random Observation Schemes -- Bootstrapping Asymptotic Laws -- Testing for (Common) Jumps Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=148176 High-Frequency Statistics with Asynchronous and Irregular Data [document électronique] / Martin, Ole, ; SpringerLink (Online service) . - Heidelberg (DEU) ; Wiesbaden (DEU) : Springer Spektrum, 2019 . - XIII, 323 p. 34 illus : online resource. - (Mathematische Optimierung und Wirtschaftsmathematik = Mathematical Optimization and Economathematics, ISSN 2523-7926) .ISBN: 978-3-658-28418-3

Langues : Anglais (eng)

Tags : Probabilities Statistics Economics Mathematical Probability Theory and Stochastic Processes Statistics for Business Management Finance Insurance Quantitative Finance Résumé : Ole Martin extends well-established techniques for the analysis of high-frequency data based on regular observations to the more general setting of asynchronous and irregular observations. Such methods are much needed in practice as real data usually comes in irregular form. In the theoretical part he develops laws of large numbers and central limit theorems as well as a new bootstrap procedure to assess asymptotic laws. The author then applies the theoretical results to estimate the quadratic covariation and to construct tests for the presence of common jumps. The simulation results show that in finite samples his methods despite the much more complex setting perform comparably well as methods based on regular data. Contents Laws of Large Numbers Random Observation Schemes Bootstrapping Asymptotic Laws Testing for (Common) Jumps Target Groups Scientists and students in the field of mathematical statistics, econometrics and financial mathematics Practitioners in the field of financial mathematics About the Author Dr. Ole Martin completed his PhD at the Kiel University (CAU), Germany. His research focuses on high-frequency statistics for semimartingales with the aim to develop methods based on irregularly observed data Note de contenu : Laws of Large Numbers -- Random Observation Schemes -- Bootstrapping Asymptotic Laws -- Testing for (Common) Jumps Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=148176 The Palgrave Handbook of Economic Performance Analysis / SpringerLink (Online service) ; Thijs ten Raa ; William H. Greene / Basingstoke (GBR) ; Londres ; New York : Palgrave MacMillan (2019)

Titre : The Palgrave Handbook of Economic Performance Analysis Type de document : document électronique Auteurs : SpringerLink (Online service) ; Thijs ten Raa, ; William H. Greene (1951-....), Editeur : Basingstoke (GBR) ; Londres ; New York : Palgrave MacMillan Année de publication : 2019 Importance : XIII, 759 p. 37 illus., 17 illus. in color Présentation : online resource ISBN/ISSN/EAN : 978-3-030-23727-1 Langues : Anglais ( eng)Tags : Econometrics Microeconomics Macroeconomics Statistics Macroeconomics/Monetary Economics//Financial Economics Statistics for Business Management Economics Finance Insurance Résumé : This Handbook takes an econometric approach to the foundations of economic performance analysis. The focus is on the measurement of efficiency, productivity, growth and performance. These concepts are commonly measured residually and difficult to quantify in practice. In real-life applications, efficiency and productivity estimates are often quite sensitive to the models used in the performance assessment and the methodological approaches adopted by the analysis. The Palgrave Handbook of Performance Analysis discusses the two basic techniques of performance measurement – deterministic benchmarking and stochastic benchmarking – in detail, and addresses the statistical techniques that connect them. All chapters include applications and explore topics ranging from the output/input ratio to productivity indexes and national statistics Note de contenu : 1. Introduction- William H. Greene and Thijs ten Raa -- 2. Micro foundations of earnings differences- Tirthatanmoy Das and Solomon W. Polachek -- 3. Performance: The output/input ratio- Thijs ten Raa -- 4. R&D, innovation and productivity- Pierre Mohnen -- 5. The choice of comparable DMUs and environmental variables- John Ruggiero -- 6. Data Envelopment Analysis with alternative returns to scale- Subhash Ray -- 7. Ranking methods within Data Envelopment Analysis- Nicole Adler and Nicola Volta -- 8. Distributional forms in Stochastic Frontier Analysis- Alexander D. Stead, Phill Wheat, and William H. Greene -- 9. Stochastic frontier models for discrete output variables- Eduardo Fe -- 10. Nonparametric statistical analysis of production- Camilla Mastromarco, Leopold Simar, and Paul W. Wilson -- 11. Bayesian performance evaluation- Mike G. Tsionas -- 12. Common methodological choices in parametric and non-parametric analyses of firms' performance- Luis Orea/Jose Zofio -- 13. Pricing inputs and outputs: Market prices versus shadow prices, market power, and welfare analysis- Aditi Bhattacharyya, Levent Kutlu, and Robin C. Sickles -- 14. Aggregation of individual efficiency measures and productivity indices- Andreas Mayer and Valentin Zelenyuk -- 15. Intermediate inputs and industry studies: Input-Output Analysis- Victoria Shestalova -- 16. Modelling environmental adjustments of production technologies: A literature review- K. Hervé Dakpo and Frederic Ang -- 17. An overview of issues in measuring the performance of national economies- Anthony Glass, Karligash Kenjegalieva, Robin C Sickles, and Thomas Weyman-Jones -- 18. Productivity indexes and national statistics: Theory, methods and challenges- W. Erwin Diewert and Kevin J. Fox. Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=149019 The Palgrave Handbook of Economic Performance Analysis [document électronique] / SpringerLink (Online service) ; Thijs ten Raa, ; William H. Greene (1951-....), . - Basingstoke (GBR) ; Londres ; New York : Palgrave MacMillan, 2019 . - XIII, 759 p. 37 illus., 17 illus. in color : online resource.ISBN: 978-3-030-23727-1

Langues : Anglais (eng)

Tags : Econometrics Microeconomics Macroeconomics Statistics Macroeconomics/Monetary Economics//Financial Economics Statistics for Business Management Economics Finance Insurance Résumé : This Handbook takes an econometric approach to the foundations of economic performance analysis. The focus is on the measurement of efficiency, productivity, growth and performance. These concepts are commonly measured residually and difficult to quantify in practice. In real-life applications, efficiency and productivity estimates are often quite sensitive to the models used in the performance assessment and the methodological approaches adopted by the analysis. The Palgrave Handbook of Performance Analysis discusses the two basic techniques of performance measurement – deterministic benchmarking and stochastic benchmarking – in detail, and addresses the statistical techniques that connect them. All chapters include applications and explore topics ranging from the output/input ratio to productivity indexes and national statistics Note de contenu : 1. Introduction- William H. Greene and Thijs ten Raa -- 2. Micro foundations of earnings differences- Tirthatanmoy Das and Solomon W. Polachek -- 3. Performance: The output/input ratio- Thijs ten Raa -- 4. R&D, innovation and productivity- Pierre Mohnen -- 5. The choice of comparable DMUs and environmental variables- John Ruggiero -- 6. Data Envelopment Analysis with alternative returns to scale- Subhash Ray -- 7. Ranking methods within Data Envelopment Analysis- Nicole Adler and Nicola Volta -- 8. Distributional forms in Stochastic Frontier Analysis- Alexander D. Stead, Phill Wheat, and William H. Greene -- 9. Stochastic frontier models for discrete output variables- Eduardo Fe -- 10. Nonparametric statistical analysis of production- Camilla Mastromarco, Leopold Simar, and Paul W. Wilson -- 11. Bayesian performance evaluation- Mike G. Tsionas -- 12. Common methodological choices in parametric and non-parametric analyses of firms' performance- Luis Orea/Jose Zofio -- 13. Pricing inputs and outputs: Market prices versus shadow prices, market power, and welfare analysis- Aditi Bhattacharyya, Levent Kutlu, and Robin C. Sickles -- 14. Aggregation of individual efficiency measures and productivity indices- Andreas Mayer and Valentin Zelenyuk -- 15. Intermediate inputs and industry studies: Input-Output Analysis- Victoria Shestalova -- 16. Modelling environmental adjustments of production technologies: A literature review- K. Hervé Dakpo and Frederic Ang -- 17. An overview of issues in measuring the performance of national economies- Anthony Glass, Karligash Kenjegalieva, Robin C Sickles, and Thomas Weyman-Jones -- 18. Productivity indexes and national statistics: Theory, methods and challenges- W. Erwin Diewert and Kevin J. Fox. Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=149019 Machine Learning in Finance / Matthew F. Dixon / Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer (2020)

Titre : Machine Learning in Finance : From Theory to Practice Type de document : document électronique Auteurs : Matthew F. Dixon, ; Igor Halperin, ; SpringerLink (Online service) ; Paul Bilokon, Editeur : Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer Année de publication : 2020 Importance : XXV, 548 p. 97 illus., 83 illus. in color Présentation : online resource ISBN/ISSN/EAN : 978-3-030-41068-1 Langues : Anglais ( eng)Tags : Statistics Applied mathematics Engineering mathematics Statistics for Business Management Economics Finance Insurance Applications of Mathematics Résumé : This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance. Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance Note de contenu : Chapter 1. Introduction -- Chapter 2. Probabilistic Modeling -- Chapter 3. Bayesian Regression & Gaussian Processes -- Chapter 4. Feed Forward Neural Networks -- Chapter 5. Interpretability -- Chapter 6. Sequence Modeling -- Chapter 7. Probabilistic Sequence Modeling -- Chapter 8. Advanced Neural Networks -- Chapter 9. Introduction to Reinforcement learning -- Chapter 10. Applications of Reinforcement Learning -- Chapter 11. Inverse Reinforcement Learning and Imitation Learning -- Chapter 12. Frontiers of Machine Learning and Finance Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=150174 Machine Learning in Finance : From Theory to Practice [document électronique] / Matthew F. Dixon, ; Igor Halperin, ; SpringerLink (Online service) ; Paul Bilokon, . - Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer, 2020 . - XXV, 548 p. 97 illus., 83 illus. in color : online resource.ISBN: 978-3-030-41068-1

Langues : Anglais (eng)

Tags : Statistics Applied mathematics Engineering mathematics Statistics for Business Management Economics Finance Insurance Applications of Mathematics Résumé : This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance. Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance Note de contenu : Chapter 1. Introduction -- Chapter 2. Probabilistic Modeling -- Chapter 3. Bayesian Regression & Gaussian Processes -- Chapter 4. Feed Forward Neural Networks -- Chapter 5. Interpretability -- Chapter 6. Sequence Modeling -- Chapter 7. Probabilistic Sequence Modeling -- Chapter 8. Advanced Neural Networks -- Chapter 9. Introduction to Reinforcement learning -- Chapter 10. Applications of Reinforcement Learning -- Chapter 11. Inverse Reinforcement Learning and Imitation Learning -- Chapter 12. Frontiers of Machine Learning and Finance Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=150174 Improved Classification Rates for Localized Algorithms under Margin Conditions / Blaschzyk, Ingrid Karin / Wiesbaden (DEU) : Springer Fachmedien (2020)

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