<p><b>Master advanced topics in the analysis of large, dynamically dependent datasets with this insightful resource</b></p><p><i>Statistical Learning with Big Dependent Data</i> delivers a comprehensive presentation of the statistical and machine learning methods useful for analyzing and forecasting large and dynamically dependent data sets. The book presents automatic procedures for modelling and forecasting large sets of time series data. Beginning with some visualization tools, the book discusses procedures and methods for finding outliers, clusters, and other types of heterogeneity in big dependent data. It then introduces various dimension reduction methods, including regularization and factor models such as regularized Lasso in the presence of dynamical dependence and dynamic factor models. The book also covers other forecasting procedures, including index models, partial least squares, boosting, and now-casting. It further presents machine-learning methods, including neural netw
Pris: kr 1479.00 fra Norli
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This book provides a broad, mature, and systematic introduction to current financial econometric models and their applications to modeling and prediction of financial time series data. It utilizes real-world examples and real financial data throughout...
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<p><b>A complete set of statistical tools for beginning financial analysts from a leading authority</b></p><p>Written by one of the leading experts on the topic, <i>An Introduction to Analysis of Financial Data with...
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The theory of data assimilation and machine learning is introduced in an accessible and pedagogical manner, with a focus on the underlying statistical physics. This modern and cross-disciplinary book is suitable for undergraduate and graduate students...
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This concise introduction covers inverse problems and data assimilation, before exploring their inter-relations. Suitable for both classroom teaching and self-guided study, it is aimed at advanced undergraduates and beginning graduate students in mathematical...
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