Snigdhansu Chatterjee – författare
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4 produkter
4 produkter
Inbunden, Engelska, 2018
657 kr
Skickas inom 10-15 vardagar
This is an introductory text on a broad class of statistical estimators that are minimizers of convex functions. It covers the basics of U-statistics and Mm-estimators and develops their asymptotic properties. It also provides an elementary introduction to resampling, particularly in the context of these estimators. The last chapter is on practical implementation of the methods presented in other chapters, using the free software R.
E-bok
PDF, Engelska, 2018870 kr
Läs direkt efter köp
This is an introductory text on a broad class of statistical estimators that are minimizers of convex functions. It covers the basics of U-statistics and Mm-estimators and develops their asymptotic properties. It also provides an elementary introduction to resampling, particularly in the context of these estimators. The last chapter is on practical implementation of the methods presented in other chapters, using the free software R.
Häftad, Engelska, 2019
657 kr
Skickas inom 10-15 vardagar
This is an introductory text on a broad class of statistical estimators that are minimizers of convex functions. It covers the basics of U-statistics and Mm-estimators and develops their asymptotic properties. It also provides an elementary introduction to resampling, particularly in the context of these estimators. The last chapter is on practical implementation of the methods presented in other chapters, using the free software R.
Inbunden, Engelska, 2027
1 696 kr
Kommande
This edited volume is a Data Science text, where multiple aspects of Statistics, Machine Learning, Artificial Intelligence, Big Data methodology, High-dimensional techniques and algorithms, and applications and case studies are presented together. Owing to its very broad scope, the chapters of this book will be collected under thematically coherent groups. The planned groups of chapters are on (i) Regularization and high-dimensional machine learning, (ii) Bayesian high-dimensional modeling and computation, (iii) Spatio-temporal and dependent data models, and (iv) Deep learning and artificial intelligence. Case studies and applications, as well as high-dimensional probability theory may be two other groups of chapters, if a number of authors write with primary focus on these topics. This book will be useful for graduate students who want to specialize eventually on some aspect of Data Science, to beginners as well as advanced researchers in the field of Data Science, and mayas well serve as an encyclopedic text on Data Science.