03989nam a22004575i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050001400153072001600167072002300183072001500206245019500221250001800416264006900434300004300503336002600546337002600572338003600598347002400634490006400658505096900722520126901691650001802960650002702978650001603005650002403021650005103045700007903096700007803175710003403253773002603287776003603313776003603349776003603385830006403421856004603485978-3-031-69622-0DE-He21320251030114623.0cr nn 008mamaa241218s2024    sz |    s    |||| 0|eng d  a97830316962207 a10.1007/978-3-031-69622-02doi 4aQA276-280 7aPBT2bicssc 7aMAT0290002bisacsh 7aPBT2thema10aStatistical Modeling and Applicationsh[electronic resource] :bMultivariate, Heavy-Tailed, Skewed Distributions and Mixture Modeling, Volume 2 /cedited by Carlos A. Coelho, Ding-Geng Chen.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aXX, 250 p. 97 illus.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aEmerging Topics in Statistics and Biostatistics,x2524-77430 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.  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. 0aStatistics . 0aSampling (Statistics).14aStatistics.24aApplied Statistics.24aMethodology of Data Collection and Processing.1 aCoelho, Carlos A.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt1 aChen, Ding-Geng.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303169621308iPrinted edition:z978303169623708iPrinted edition:z9783031696244 0aEmerging Topics in Statistics and Biostatistics,x2524-774340uhttps://doi.org/10.1007/978-3-031-69622-0