Author: Jianqing Fan
Posted on 2020-05-22, by freebook77.
Statistical Foundations of Data Science gives a thorough introduction to commonly used statistical models, contemporary statistical machine learning techniques and algorithms, along with their mathematical insights and statistical theories. It aims to serve as a graduate-level textbook and a research monograph on high-dimensional statistics, sparsity and covariance learning, machine learning, and statistical inference. It includes ample exercises that involve both theoretical studies as well as empirical applications.
begins with an introduction to the stylized features of big data and
their impacts on statistical analysis. It then introduces multiple
linear regression and expands the techniques of model building via
nonparametric regression and kernel tricks. It provides a comprehensive
account on sparsity explorations and model selections for multiple
regression, generalized linear models, quantile regression, robust
regression, hazards regression, among others. High-dimensional inference
is also thoroughly addressed and so is feature screening. The book also
provides a comprehensive account on high-dimensional covariance
estimation, learning latent factors and hidden structures, as well as
their applications to statistical estimation, inference, prediction and
machine learning problems. It also introduces thoroughly statistical
machine learning theory and methods for classification, clustering, and
prediction. These include CART, random forests, boosting, support vector
machines, clustering algorithms, sparse PCA, and deep learning.
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