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Statistical modeling and applications = multivariate, heavy-tailed, skewed distributions and mixture modeling.. Volume 2 /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Statistical modeling and applications/ edited by Carlos A. Coelho, Ding-Geng Chen.
其他題名:	multivariate, heavy-tailed, skewed distributions and mixture modeling.
其他作者:	Coelho, Carlos A.
出版者:	Cham :Springer Nature Switzerland : : 2024.,
面頁冊數:	xx, 250 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Statistics. -
電子資源:	https://doi.org/10.1007/978-3-031-69622-0
ISBN:	9783031696220

Statistical modeling and applications = multivariate, heavy-tailed, skewed distributions and mixture modeling.. Volume 2 /
Statistical modeling and applicationsmultivariate, heavy-tailed, skewed distributions and mixture modeling.Volume 2 /[electronic resource] :edited by Carlos A. Coelho, Ding-Geng Chen. - Cham :Springer Nature Switzerland :2024. - xx, 250 p. :ill., digital ;24 cm. - Emerging topics in statistics and biostatistics,2524-7743. - Emerging topics in statistics and biostatistics..

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.

ISBN: 9783031696220

Standard No.: 10.1007/978-3-031-69622-0doiSubjects--Topical Terms:

517247
Statistics.

LC Class. No.: QA276

Dewey Class. No.: 519.54

Statistical modeling and applications = multivariate, heavy-tailed, skewed distributions and mixture modeling.. Volume 2 /
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