Complex Models and Computational Methods in Statistics

By Matteo Grigoletto

Book Overview

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.

• Format: Paperback
• Language: English
• ISBN: 8847055652
• ISBN13: 9788847055650
• Release Date: February 2015
• Publisher: Springer
• Length: 228 Pages
• Weight: 0.74 lbs.
• Dimensions: 0.5" x 6.1" x 9.2"