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245 1 0 _aStatistical Modeling and Applications
_h[electronic resource] :
_bMultivariate, Heavy-Tailed, Skewed Distributions and Mixture Modeling, Volume 2 /
_cedited by Carlos A. Coelho, Ding-Geng Chen.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aXX, 250 p. 97 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
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347 _atext file
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490 1 _aEmerging Topics in Statistics and Biostatistics,
_x2524-7743
505 0 _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.
520 _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.
650 0 _aStatistics .
_965982
650 0 _aSampling (Statistics).
_959805
650 1 4 _aStatistics.
_965982
650 2 4 _aApplied Statistics.
_965986
650 2 4 _aMethodology of Data Collection and Processing.
_965946
700 1 _aCoelho, Carlos A.
_eeditor.
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_4http://id.loc.gov/vocabulary/relators/edt
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700 1 _aChen, Ding-Geng.
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710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
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830 0 _aEmerging Topics in Statistics and Biostatistics,
_x2524-7743
_988892
856 4 0 _uhttps://doi.org/10.1007/978-3-031-69622-0
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