Statistical Modeling and Applications

In an era defined by the seamless integration of data and sophisticated analytical and modeling techniques, the quest for advanced statistical modeling and methodologies has never been more pertinent. Statistical Modeling and Applications: Multivariate, Heavy-Tailed, Skewed Distributions, Mixture and Neural-Network Modeling , Volume 2, represents a concerted effort to bridge the gap between theoretical advancements and practical applications in the realm of Statistical Science, namely in the area of Statistical Modeling. It also aims to present a wide range of emerging topics in mathematical and statistical modeling written by a group of distinguished researchers from top-tier universities and research institutes to offer broader opportunities in stimulating further collaborations in the areas of mathematics and statistics.

The book has eleven chapters, divided in two Parts, with Part I comprising five chapters dealing with the application of Multivariate Analysis techniques and multivariate distributions to a set of different situations, and Part II consisting of six chapters which address the modeling of several interesting phenomena through the use of Heavy-Tailed, Skewed, Circular-Linear and Mixture Distributions, as well as Neural Networks.

Presents cutting-edge evidence-based public health research Includes mathematical and statistical modeling with applications to real-world applications Presents a wide range of emerging topics in mathematical and statistical modeling written by a group of researchers

Auteur

Carlos Coelho is a Professor of Statistics at the Mathematics Department of NOVA School of Science and Technology of NOVA University of Lisbon. His main area of research is Multivariate Analysis, namely the development of likelihood ratio tests for elaborate covariance structures and for MANOVA models, also with elaborate covariance structures, together with the study of the exact distribution and the development of near-exact distributions for the associated test statistics. Related with this area, other areas of interest are Mathematical Statistics and Distribution Theory, as well as Estimation, Univariate and Multivariate Linear, Generalized Linear and Mixed Models. More recently, he also got interested in tests for high-dimensionality and the application of Multivariate Analysis techniques to Statistical Disclosure Control problems. He is Associate Editor of the Springer Book series "Emerging Topics in Statistics and Biostatistics" and a member of the International Council of the "Business World" Library of the Tsenov Academy of Economics (Svishtov, Bulgaria).

Ding-Geng Chen is a fellow of the American Statistical Association and is currently the executive director and professor in biostatistics at the College of Health Solutions, Arizona State University. He is also an extraordinary professor and the SARChI in biostatistics at the University of Pretoria, an honorary professor at the University of KwaZulu-Natal, South Africa. Dr. Chen was the Karl E. Peace Endowed Eminent Scholar Chair in Biostatistics at Georgia Southern University. He is a senior biostatistics consultant for biopharmaceuticals and government agencies with extensive expertise in biostatistics, clinical trials, and public health statistics. Dr. Chen has more than 200 referred professional publications and co-authored and co-edited 35 books on clinical trial methodology, meta-analysis, data science, causal inference, and public health research.

Contenu

.- Random Gaussian fields and systems of stochastic partial differential equations.
.- A Poly-cylindrical Bayesian network for clustering oceanographic data.
.- A Copula-Based Approach to Statistical Modelling of Solar Irradiance.
.- Two-sample intraclass correlation coefficient tests for matrix-valued data.
.- Evolution of the generation and analysis of single imputation synthetic datasets in Statistical Disclosure Control.
.- Some empirical findings on neural network-based forecasting when subjected to autoregressive resampling.
.- Enriched lognormal models for income data:A new approach to estimate semi-parametric Gaussian mixtures of regressions with varying mixing proportions.
.- Computational comparisons of two-component mixtures using Lindley-type models.
.- Baranchik-type estimators under modified balanced loss functions.
.- Modelling the movement of a South African cheetah using a hidden Markov model and circular-linear regression.