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Sinopsis
A Theory-based Lasso for Time-Series Data.- Invariance-Based Explanation.- Composition of Quantum Operations and Their Fixed Points.- Information quality: the contribution of fuzzy methods.- Parameter-Centric Analysis Grossly Exaggerates Certainty.- Three Approaches to the Comparison of Random Variables.- A QP framework: a contextual representation of agents' preferences in investment choice.- How to Make a Decision Based on the Minimum Bayes Factor (MBF): Explanation of the Jeffreys Scale.- Extending the A Priori Procedure (APP) to Address Correlation Coefficients.- Variable Selection and Estimation in Kink Regression Model.- Performance of microfinance institutions in Vietnam.- Factors Influencing on University Reputation in Viet Nam: Model Selection by AIC.- Impacts of Internal and External Macro Factors on Firm Stock Price in an Expansion Econometric Model - A Case in Vietnam Real Estate Industry.- How Values Influence Economic Progress? An Evidence from South And Southeast Asian Countries.- The Effect of Governance Characteristics on Firm Performance: Evidence from Vietnam.- Does Capital Affect Bank Risk in Vietnam: A Bayesian Approach.
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De la contraportada
This book offers an overview of state-of-the-art econometric techniques, with a special emphasis on financial econometrics. There is a major need for such techniques, since the traditional way of designing mathematical models - based on researchers' insights - can no longer keep pace with the ever-increasing data flow. To catch up, many application areas have begun relying on data science, i.e., on techniques for extracting models from data, such as data mining, machine learning, and innovative statistics. In terms of capitalizing on data science, many application areas are way ahead of economics. To close this gap, the book provides examples of how data science techniques can be used in economics. Corresponding techniques range from almost traditional statistics to promising novel ideas such as quantum econometrics. Given its scope, the book will appeal to students and researchers interested in state-of-the-art developments, and to practitioners interested in using data science techniques.
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- EditorialSpringer
- Año de publicación2021
- ISBN 10 3030488551
- ISBN 13 9783030488550
- EncuadernaciónTapa blanda
- IdiomaInglés
- Número de edición1
- Número de páginas644
- EditorNgoc Thach Nguyen, Kreinovich Vladik, Trung Nguyen Duc
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Data Science for Financial Econometrics
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Springer International Publishing, 2021
ISBN 10: 3030488551
ISBN 13: 9783030488550
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Librería: Buchpark, Trebbin, Alemania
Calificación del vendedor: 5 de 5 estrellas
Condición: Hervorragend. Zustand: Hervorragend | Seiten: 644 | Sprache: Englisch | Produktart: Bücher. Nº de ref. del artículo: 38226623/1
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Data Science for Financial Econometrics
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Springer, Berlin|Springer International Publishing|Springer, 2021
ISBN 10: 3030488551
ISBN 13: 9783030488550
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Librería: moluna, Greven, Alemania
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. To catch up, many application areas have begun relying on data science, i.e., on techniques for extracting models from data, such as data mining, machine learning, and innovative statistics.This book offers an overview of state-of-the-art econometric. Nº de ref. del artículo: 519855370
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Data Science for Financial Econometrics
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Springer International Publishing Nov 2021, 2021
ISBN 10: 3030488551
ISBN 13: 9783030488550
Nuevo
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book offers an overview of state-of-the-art econometric techniques, with a special emphasis on financial econometrics. There is a major need for such techniques, since the traditional way of designing mathematical models - based on researchers' insights - can no longer keep pace with the ever-increasing data flow. To catch up, many application areas have begun relying on data science, i.e., on techniques for extracting models from data, such as data mining, machine learning, and innovative statistics. In terms of capitalizing on data science, many application areas are way ahead of economics. To close this gap, the book provides examples of how data science techniques can be used in economics. Corresponding techniques range from almost traditional statistics to promising novel ideas such as quantum econometrics. Given its scope, the book will appeal to students and researchers interested in state-of-the-art developments, and to practitioners interested in using data science techniques. 644 pp. Englisch. Nº de ref. del artículo: 9783030488550
Cantidad disponible: 2 disponibles
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Data Science for Financial Econometrics
Publicado por
Springer International Publishing, 2021
ISBN 10: 3030488551
ISBN 13: 9783030488550
Nuevo
Taschenbuch
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book offers an overview of state-of-the-art econometric techniques, with a special emphasis on financial econometrics. There is a major need for such techniques, since the traditional way of designing mathematical models - based on researchers' insights - can no longer keep pace with the ever-increasing data flow. To catch up, many application areas have begun relying on data science, i.e., on techniques for extracting models from data, such as data mining, machine learning, and innovative statistics. In terms of capitalizing on data science, many application areas are way ahead of economics. To close this gap, the book provides examples of how data science techniques can be used in economics. Corresponding techniques range from almost traditional statistics to promising novel ideas such as quantum econometrics. Given its scope, the book will appeal to students and researchers interested in state-of-the-art developments, and to practitioners interested in using data science techniques. Nº de ref. del artículo: 9783030488550
Cantidad disponible: 1 disponibles
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Data Science for Financial Econometrics
Publicado por
Springer International Publishing, Springer International Publishing Nov 2021, 2021
ISBN 10: 3030488551
ISBN 13: 9783030488550
Nuevo
Taschenbuch
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book offers an overview of state-of-the-art econometric techniques, with a special emphasis on financial econometrics. There is a major need for such techniques, since the traditional way of designing mathematical models ¿ based on researchers¿ insights ¿ can no longer keep pace with the ever-increasing data flow. To catch up, many application areas have begun relying on data science, i.e., on techniques for extracting models from data, such as data mining, machine learning, and innovative statistics. In terms of capitalizing on data science, many application areas are way ahead of economics. To close this gap, the book provides examples of how data science techniques can be used in economics. Corresponding techniques range from almost traditional statistics to promising novel ideas such as quantum econometrics. Given its scope, the book will appeal to students and researchers interested in state-of-the-art developments, and to practitioners interested in using data science techniques.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 644 pp. Englisch. Nº de ref. del artículo: 9783030488550
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Data Science for Financial Econometrics (Studies in Computational Intelligence, 898)
Librería: Books Puddle, New York, NY, Estados Unidos de America
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Condición: New. 1st ed. 2021 edition NO-PA16APR2015-KAP. Nº de ref. del artículo: 26390222109
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Impresión bajo demandaLibrería: Majestic Books, Hounslow, Reino Unido
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Impresión bajo demandaLibrería: Biblios, Frankfurt am main, HESSE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Condición: New. PRINT ON DEMAND. Nº de ref. del artículo: 18390222103
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