Ebook: Complex Models and Computational Methods in Statistics
Author: Laura Azzimonti Francesca Ieva (auth.) Matteo Grigoletto Francesco Lisi Sonia Petrone (eds.)
- Tags: Statistical Theory and Methods, Mathematics in the Humanities and Social Sciences, Economics/Management Science general, Biostatistics
- Series: Contributions to Statistics
- Year: 2013
- Publisher: Springer-Verlag Mailand
- Edition: 1
- Language: English
- pdf
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.
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.
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.
Content:
Front Matter....Pages i-viii
A New Unsupervised Classification Technique Through Nonlinear Non Parametric Mixed-Effects Models....Pages 1-11
Estimation Approaches for the Apparent Diffusion Coefficient in Rice-Distributed MR Signals....Pages 13-26
Longitudinal Patterns of Financial Product Ownership: A Latent Growth Mixture Approach....Pages 27-36
Computationally Efficient Inference Procedures for Vast Dimensional Realized Covariance Models....Pages 37-49
A GPU Software Library for Likelihood-Based Inference of Environmental Models with Large Datasets....Pages 51-62
Theoretical Regression Trees: A Tool for Multiple Structural-Change Models Analysis....Pages 63-76
Some Contributions to the Theory of Conditional Gibbs Partitions....Pages 77-89
Estimation of Traffic Matrices for LRD Traffic....Pages 91-107
A Newton’s Method for Benchmarking Time Series....Pages 109-121
Spatial Smoothing for Data Distributed over Non-planar Domains....Pages 123-135
Volatility Swings in the US Financial Markets....Pages 137-148
Semicontinuous Regression Models with Skew Distributions....Pages 149-160
Classification of Multivariate Linear-Circular Data with Nonignorable Missing Values....Pages 161-173
Multidimensional Connected Set Detection in Clustering Based on Nonparametric Density Estimation....Pages 175-186
Using Integrated Nested Laplace Approximations for Modelling Spatial Healthcare Utilization....Pages 187-201
Supply Function Prediction in Electricity Auctions....Pages 203-213
A Hierarchical Bayesian Model for RNA-Seq Data....Pages 215-227
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.
Content:
Front Matter....Pages i-viii
A New Unsupervised Classification Technique Through Nonlinear Non Parametric Mixed-Effects Models....Pages 1-11
Estimation Approaches for the Apparent Diffusion Coefficient in Rice-Distributed MR Signals....Pages 13-26
Longitudinal Patterns of Financial Product Ownership: A Latent Growth Mixture Approach....Pages 27-36
Computationally Efficient Inference Procedures for Vast Dimensional Realized Covariance Models....Pages 37-49
A GPU Software Library for Likelihood-Based Inference of Environmental Models with Large Datasets....Pages 51-62
Theoretical Regression Trees: A Tool for Multiple Structural-Change Models Analysis....Pages 63-76
Some Contributions to the Theory of Conditional Gibbs Partitions....Pages 77-89
Estimation of Traffic Matrices for LRD Traffic....Pages 91-107
A Newton’s Method for Benchmarking Time Series....Pages 109-121
Spatial Smoothing for Data Distributed over Non-planar Domains....Pages 123-135
Volatility Swings in the US Financial Markets....Pages 137-148
Semicontinuous Regression Models with Skew Distributions....Pages 149-160
Classification of Multivariate Linear-Circular Data with Nonignorable Missing Values....Pages 161-173
Multidimensional Connected Set Detection in Clustering Based on Nonparametric Density Estimation....Pages 175-186
Using Integrated Nested Laplace Approximations for Modelling Spatial Healthcare Utilization....Pages 187-201
Supply Function Prediction in Electricity Auctions....Pages 203-213
A Hierarchical Bayesian Model for RNA-Seq Data....Pages 215-227
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