Complex Models and Computational Methods in Statistics

"Complex Models and Computational Methods in Statistics has a good collection of research papers on useful and interesting topics about complex and high-dimensional data. Researchers and professionals looking to learn more in this field of study could benefit from papers published in this volume. The content in most of these selected papers proved to be an enjoyable read." (Technometrics, Vol. 56 (3), August, 2014)

• 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.