Sanjeev kulkarni (58 risultati)

Hardcover. Condizione: Very Good. 1. With dust jacket. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

Condizione: Very Good. Very Good Condition. Has some wear. Five star seller - Buy with confidence.

Condizione: Very Good. 1st Edition. Former library copy. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Includes library markings. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.…

Condizione: Very Good. 1st Edition. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

Condizione: very_good. This book is in Very good condition. There may be a few flaws like shelf wear and some light wear.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Paperback. Condizione: New.

Condizione: New. In English.

Condizione: New.

Paperback. Condizione: Brand New. 120 pages. 7.50x5.10x0.50 inches. In Stock.

Condizione: As New. Unread book in perfect condition.

Hardback. Condizione: Good. The modern financial industry has been required to deal with large and diverse portfolios in a variety of asset classes often with limited market data available.

Condizione: Very Good. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

Condizione: very_good. Book is in very good condition and may include minimal underlining highlighting. The book can also include "From the library of" labels. May not contain miscellaneous items toys, dvds, etc. . We offer 100% money back guarantee and 24 7 customer service.

Condizione: good. Fast Free Shipping â" Good condition. It may show normal signs of use, such as light writing, highlighting, or library markings, but all pages are intact and the book is fully readable. A solid, complete copy that's ready to enjoy.

HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Condizione: new.

Paperback. Condizione: New.

HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Condizione: New.

Condizione: New.

Condizione: New.

Condizione: New. pp. 232.

Condizione: New. In English.

Condizione: New. pp. 232 Index.

Condizione: As New. Unread book in perfect condition.

Hardback. Condizione: New. The modern financial industry has been required to deal with large and diverse portfolios in a variety of asset classes often with limited market data available. Financial Signal Processing and Machine Learning unifies a number of recent advances made in signal processing and machine learning for the design and management of investment portfolios and financial engineering. This book bridges the gap between these disciplines, offering the latest information on key topics including characterizing statistical dependence and correlation in high dimensions, constructing effective and robust risk measures, and their use in portfolio optimization and rebalancing. The book focuses on signal processing approaches to model return, momentum, and mean reversion, addressing theoretical and implementation aspects. It highlights the connections between portfolio theory, sparse learning and compressed sensing, sparse eigen-portfolios, robust optimization, non-Gaussian data-driven risk measures, graphical models, causal analysis through temporal-causal modeling, and large-scale copula-based approaches. Key features: Highlights signal processing and machine learning as key approaches to quantitative finance.Offers advanced mathematical tools for high-dimensional portfolio construction, monitoring, and post-trade analysis problems.Presents portfolio theory, sparse learning and compressed sensing, sparsity methods for investment portfolios. including eigen-portfolios, model return, momentum, mean reversion and non-Gaussian data-driven risk measures with real-world applications of these techniques.Includes contributions from leading researchers and practitioners in both the signal and information processing communities, and the quantitative finance community.…