| Statistical Machine Learning with Applications in Finance Contributor(s): Ritter, Gordon (Author) |
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ISBN: 9811232334 ISBN-13: 9789811232336 Publisher: World Scientific Publishing Company OUR PRICE: $121.60 Product Type: Hardcover Published: December 2024 This item may be ordered no more than 25 days prior to its publication date of December 30, 2024 |
| Additional Information |
| BISAC Categories: - Mathematics | Probability & Statistics - General |
| Physical Information: 480 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: This unique compendium develops a general approach to building models of economic and financial processes, with a focus on statistical learning techniques that scale to large data sets. It introduces the key elements of a parametric statistical model: likelihood, prior, and posterior, and show how to use them to make predictions.The book covers classical techniques such as multiple regression and the Kalman filter in a clear, accessible style that has been popular with students, but also includes detailed treatments of state-of-the-art models, highlighting tree-based methods, support vector machines and kernel methods, deep learning, and reinforcement learning. Theories are supplemented by real-world examples.This reference text is useful for undergraduate, graduate and even PhD students in quantitative finance, and also to practitioners who are facing the reality that data science and machine learning are disrupting the industry. |