Titlebook: Handbook of Computational Statistics; Concepts and Methods James E. Gentle,Wolfgang Karl Härdle,Yuichi Mori Book 2012Latest edition Springe

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Discovering and Visualizing Relations in High Dimensional Datay can not only cut great swaths searching through this combinatorial explosion but also extract insights from the visual patterns. These are the core reasons for data visualization. With Parallel Coordinates (abbr.-cs) the search for multivariate relations in high dimensional datasets is transformed

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Interactive and Dynamic Graphicsips in complex data. Interactive and dynamic statistical graphics involve methods for viewing data in the form of point clouds or modeled surfaces. Higher-dimensional data can be projected into one-, two- or three-dimensional planes in a set of multiple views or as a continuous sequence of views whi

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Model Selectionnear, non-linear and generalized linear) models with multiple independent variables, one needs to decide which variables to include in the model (Chaps. III.7, III.8 and III.12); for fitting non-parametric regression (spline, kernel, local polynomial) models, one needs to decide the amount of smooth

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Yoshio Yotsuyanagi,Daniel Szöllösiression reduces to solving a system of linear equations, see Chap. III.8. Theprincipal components method is based on finding eigenvalues and eigenvectors of a matrix, see Chap. III.6. Nonlinear optimization methods such as Newton’s method often employ the inversion of a Hessian matrix. In all these

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Ernst B. Hunziker,Wolfgang Herrmannriety of incomplete-data problems. The EM algorithm has a number of desirable properties, such as its numerical stability, reliable global convergence, and simplicity of implementation. There are, however, two main drawbacks of the basic EM algorithm – lack of an in-built procedure to compute the co

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