LEADER 04234nam 22006255 450 001 9910917795803321 005 20260302112721.0 010 $a9783031696220 010 $a3031696220 024 7 $a10.1007/978-3-031-69622-0 035 $a(CKB)37037232100041 035 $a(MiAaPQ)EBC31849673 035 $a(Au-PeEL)EBL31849673 035 $a(DE-He213)978-3-031-69622-0 035 $a(OCoLC)1481797090 035 $a(EXLCZ)9937037232100041 100 $a20241218d2024 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aStatistical Modeling and Applications $eMultivariate, Heavy-Tailed, Skewed Distributions and Mixture Modeling, Volume 2 /$fedited by Carlos A. Coelho, Ding-Geng Chen 205 $a1st ed. 2024. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2024. 215 $a1 online resource (260 pages) 225 1 $aEmerging Topics in Statistics and Biostatistics,$x2524-7743 311 08$a9783031696213 311 08$a3031696212 327 $a-- 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. 330 $aIn an era defined by the seamless integration of data and sophisticated analytical and modeling techniques, the quest for advanced statistical modeling and methodologies has never been more pertinent. Statistical Modeling and Applications: Multivariate, Heavy-Tailed, Skewed Distributions, Mixture and Neural-Network Modeling, Volume 2, represents a concerted effort to bridge the gap between theoretical advancements and practical applications in the realm of Statistical Science, namely in the area of Statistical Modeling. It also aims to present a wide range of emerging topics in mathematical and statistical modeling written by a group of distinguished researchers from top-tier universities and research institutes to offer broader opportunities in stimulating further collaborations in the areas of mathematics and statistics. The book has eleven chapters, divided in two Parts, with Part I comprising five chapters dealing with the application of Multivariate Analysis techniques and multivariate distributions to a set of different situations, and Part II consisting of six chapters which address the modeling of several interesting phenomena through the use of Heavy-Tailed, Skewed, Circular-Linear and Mixture Distributions, as well as Neural Networks. 410 0$aEmerging Topics in Statistics and Biostatistics,$x2524-7743 606 $aStatistics 606 $aSampling (Statistics) 606 $aStatistics 606 $aApplied Statistics 606 $aMethodology of Data Collection and Processing 606 $aMostreig (Estadística)$2thub 608 $aLlibres electrònics$2thub 615 0$aStatistics. 615 0$aSampling (Statistics) 615 14$aStatistics. 615 24$aApplied Statistics. 615 24$aMethodology of Data Collection and Processing. 615 7$aMostreig (Estadística) 676 $a519.5 700 $aCoelho$b Carlos A$0781327 701 $aChen$b Ding-Geng$0767993 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910917795803321 996 $aStatistical Modeling and Applications$94304982 997 $aUNINA