04361nam a22005415i 4500 978-3-031-69622-0 DE-He213 20251030114623.0 cr nn 008mamaa 241218s2024 sz | s |||| 0|eng d 9783031696220 978-3-031-69622-0 10.1007/978-3-031-69622-0 doi QA276-280 PBT bicssc MAT029000 bisacsh PBT thema Statistical Modeling and Applications [electronic resource] : Multivariate, Heavy-Tailed, Skewed Distributions and Mixture Modeling, Volume 2 / edited by Carlos A. Coelho, Ding-Geng Chen. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. XX, 250 p. 97 illus. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Emerging Topics in Statistics and Biostatistics, 2524-7743 -- 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. 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. Statistics . 65982 Sampling (Statistics). 59805 Statistics. 65982 Applied Statistics. 65986 Methodology of Data Collection and Processing. 65946 Coelho, Carlos A. editor. edt http://id.loc.gov/vocabulary/relators/edt 88891 Chen, Ding-Geng. editor. edt http://id.loc.gov/vocabulary/relators/edt 80220 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031696213 Printed edition: 9783031696237 Printed edition: 9783031696244 Emerging Topics in Statistics and Biostatistics, 2524-7743 88892 https://doi.org/10.1007/978-3-031-69622-0 ZDB-2-SMA ZDB-2-SXMS 65786 65786