Complex Models and Computational Methods in Statistics

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.

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.