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Complex Models and Computational Methods in Statistics de Matteo Grigoletto

Complex Models and Computational Methods in Statistics: Contributions to Statistics

Editat de Matteo Grigoletto, Francesco Lisi, Sonia Petrone
en Limba Engleză Hardback – 27 ian 2013
The use of computational methods in statistics to face complex problems and highly dimensional data, as well as the widespread availability of computer technology, is no news. The range of applications, instead, is unprecedented.
As often occurs, new and complex data types require new strategies, demanding for the development of novel statistical methods and suggesting stimulating mathematical problems.
This book is addressed to researchers working at the forefront of the statistical analysis of complex systems and using computationally intensive statistical methods.
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Specificații

ISBN-13: 9788847028708
ISBN-10: 8847028701
Pagini: 236
Ilustrații: VIII, 228 p.
Dimensiuni: 155 x 235 x 18 mm
Greutate: 0.51 kg
Ediția:2013
Editura: Springer
Colecția Springer
Seria Contributions to Statistics

Locul publicării:Milano, Italy

Public țintă

Research

Cuprins

A new unsupervised classification technique through nonlinear non parametric mixed effects models.- Estimation approaches for the apparent diffusion coefficient in Rice-distributed MR signals.- Longitudinal patterns of financial product ownership: a latent growth mixture approach.- Computationally efficient inference procedures for vast dimensional realized covariance models.- A GPU software library for likelihood-based inference of environmental models with large datasets.- Theoretical Regression Trees: a tool for multiple structural-change models analysis.- Some contributions to the theory of conditional Gibbs partitions.- Estimation of traffic matrices for LRD traffic.- A Newton's method for benchmarking time series.- Spatial smoothing for data distributed over non-planar domains.- Volatility swings in the US financial markets.- Semicontinuous regression models with skew distributions.- Classification of multivariate linear-circular data with nonignorable missing values.- Multidimensional connected set detection in clustering based on nonparametric density estimation.- Using integrated nested Laplace approximations for modelling spatial healthcare utilization.- Supply function prediction in electricity auctions.- A hierarchical bayesian model for RNA-Seq data.

Recenzii

“Complex Models and Computational Methods inStatistics has a good collection of research papers on useful and interestingtopics about complex and high-dimensional data. Researchers and professionalslooking to learn more in this field of study could benefit from paperspublished in this volume. The content in most of these selected papers provedto be an enjoyable read.” (Technometrics,Vol. 56 (3), August, 2014)

Textul de pe ultima copertă

The use of computational methods in statistics to face complex problems and highly dimensional data, as well as the widespread availability of computer technology, is no news. The range of applications, instead, is unprecedented.
As often occurs, new and complex data types require new strategies, demanding for the development of novel statistical methods and suggesting stimulating mathematical problems.
This book is addressed to researchers working at the forefront of the statistical analysis of complex systems and using computationally intensive statistical methods.

Caracteristici

The volume offers an updated overview of statistical methods for high-dimensional problems It includes a wide range of statistical applications It is addressed to the statistician working at the forefront of statistical analysis Includes supplementary material: sn.pub/extras