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Arnold Taylor


Taylor and Francis

Data premiery:


314,95 PLN
Wysyłamy w 21 dni

Opis produktu

A Computational Approach to Statistical Learning gives a novel introduction to predictive modeling by focusing on the algorithmic and numeric motivations behind popular statistical methods. The text contains annotated code to over 80 original reference functions. These functions provide minimal working implementations of common statistical learning algorithms. Every chapter concludes with a fully worked out application that illustrates predictive modeling tasks using a realworld dataset.The text begins with a detailed analysis of linear models and ordinary least squares. Subsequent chapters explore extensions such as ridge regression generalized linear models and additive models. The second half focuses on the use of generalpurpose algorithms for convex optimization and their application to tasks in statistical learning. Models covered include the elastic net dense neural networks convolutional neural networks (CNNs) and spectral clustering. A unifying theme throughout the text is the use of optimization theory in the description of predictive models with a particular focus on the singular value decomposition (SVD). Through this theme the computational approach motivates and clarifies the relationships between various predictive models.Taylor Arnold is an assistant professor of statistics at the University of Richmond. His work at the intersection of computer vision natural language processing and digital humanities has been supported by multiple grants from the National Endowment for the Humanities (NEH) and the American Council of Learned Societies (ACLS). His first book Humanities Data in R was published in 2015.Michael Kane is an assistant professor of biostatistics at Yale University. He is the recipient of grants from the National Institutes of Health (NIH) DARPA and the Bill and Melinda Gates Foundation. His R package bigmemory won the Chambers prize for statistical software in 2010.Bryan Lewis is an applied mathematician and author of many popular R packages including irlba doRedis and threejs.1. Introduction Computational approach Statistical learning Example Prerequisites How to read this book Supplementary materials Formalisms and terminology Exercises 2. Linear Models Introduction Ordinary least squares The normal equations Solving least squares with the singular value decomposition Directly solving the linear system (?) Solving linear models with orthogonal projection (?) Sensitivity analysis (?) Relationship between numerical and statistical error Implementation and notes Application: Cancer incidence rates Exercises 3. Ridge Regression and Principal Component Analysis Variance in OLS Ridge regression (?) A Bayesian perspective Principal component analysis Implementation and notes Application: NYC taxicab data Exercises 4. Linear Smoothers Nonlinearity Basis expansion Kernel regression Local regression Regression splines (?) Smoothing splines (?) Bsplines Implementation and notes Application: US census tract data Exercises 5. Genera

Data Publikacji: 2019-02-05
Wymiary: 235 mm 156 mm 662.24 gr

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