Numerical Analysis for Statisticians

Numerical analysis is the study of computation and its accuracy, stability and often its implementation on a computer. This book focuses on the principles of numerical analysis and is intended to equip those readers who use statistics to craft their own software and to understand the advantages and disadvantages of different numerical methods.

(Bookdata)

Bli först med att recensera och betygsätt boken Numerical Analysis for Statisticians - du kan vinna 200 kr varje månad i tävlingen "Månadens recension".

From the reviews: "This book provides reasonably good coverage of numerical methods that are important in statistical applications. ...but overall the text serves as a good introduction to computational statistics." - MATHEMATICAL REVIEWS From the reviews of the second edition: "This is a comprehensive handbook for anyone with an interest in computational statistics, such as instructors, statisticians, modelers, data mining analysts, and software designers. For a reader with good working knowledge of numerical analysis, the book is useful for understanding the advantages and disadvantages of different numerical methods. ! also suitable for students interested in refining their knowledge: a list of problems with gradually increasing difficulty is available, in addition to a list of very carefully chosen references (a real support for the reader)." (Dragos Calitoiu, Mathematical Reviews, Issue 2011 g)

(Bookdata)

Recurrence Relations.- Power Series Expansions.- Continued Fraction Expansions.- Asymptotic Expansions.- Solution of Nonlinear Equations.- Vector and Matrix Norms.- Linear Regression and Matrix Inversion.- Eigenvalues and Eigenvectors.- Singular Value Decomposition.- Splines.- Optimization Theory.- The MM Algorithm.- The EM Algorithm.- Newton's Method and Scoring.- Local and Global Convergence.- Advanced Optimization Topics.- Concrete Hilbert Spaces.- Quadrature Methods.- The Fourier Transform.- The Finite Fourier Transform.- Wavelets.- Generating Random Deviates.- Independent Monte Carlo.- Permutation Tests and the Bootstrap.- Finite-State Markov Chains.- Markov Chain Monte Carlo.- Advanced Topics in MCMC.

(Bookdata)