Handbook of Computational Statistics: Concepts and MethodsJames E. Gentle, Wolfgang Karl Härdle, Yuichi Mori Springer Science & Business Media, 06.07.2012 - 1192 Seiten The Handbook of Computational Statistics - Concepts and Methods (second edition) is a revision of the first edition published in 2004, and contains additional comments and updated information on the existing chapters, as well as three new chapters addressing recent work in the field of computational statistics. This new edition is divided into 4 parts in the same way as the first edition. It begins with "How Computational Statistics became the backbone of modern data science" (Ch.1): an overview of the field of Computational Statistics, how it emerged as a separate discipline, and how its own development mirrored that of hardware and software, including a discussion of current active research. The second part (Chs. 2 - 15) presents several topics in the supporting field of statistical computing. Emphasis is placed on the need for fast and accurate numerical algorithms, and some of the basic methodologies for transformation, database handling, high-dimensional data and graphics treatment are discussed. The third part (Chs. 16 - 33) focuses on statistical methodology. Special attention is given to smoothing, iterative procedures, simulation and visualization of multivariate data. Lastly, a set of selected applications (Chs. 34 - 38) like Bioinformatics, Medical Imaging, Finance, Econometrics and Network Intrusion Detection highlight the usefulness of computational statistics in real-world applications. |
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Handbook of Computational Statistics: Concepts and Methods James E. Gentle,Wolfgang Karl Härdle,Yuichi Mori Keine Leseprobe verfügbar - 2012 |
Handbook of Computational Statistics: Concepts and Methods James E. Gentle,Wolfgang Karl Härdle,Yuichi Mori Keine Leseprobe verfügbar - 2017 |
Häufige Begriffe und Wortgruppen
algebraic algorithm applications approach approximation Assoc asymptotic Bayes factor Bayesian bootstrap coefficients components Computational Statistics conditional convergence corresponding covariate data analysis data mining data set database decomposition defined denote density estimation derived dimension discussed distribution EM algorithm error example factors function Gibbs sampler given graph histogram implementation Inselberg interactive interface interval iteration kernel L’Ecuyer likelihood linear regression Markov chain matrix MCMC methods minimization model selection Monte Carlo Monte Carlo methods multivariate node nonlinear nonparametric normal normal distribution observations optimization orthogonal outliers output parallel computing parallel coordinate parameter Particle plots points polynomial problem projection pursuit QR decomposition random number regression model resampling RNGs sample scatterplot Sect simulation smoothing solution spline Springer Stat statistical graphics stochastic support vector machines techniques Theorem transform tree values variables variance varset vector visualization wavelet