Statistical Modeling and Applications Multivariate, Heavy-Tailed, Skewed Distributions and Mixture Modeling, Volume 2 /

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 an...

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-д хадгалсан:
Номзүйн дэлгэрэнгүй
Байгууллагын зохиогч: SpringerLink (Online service)
Бусад зохиолчид: Coelho, Carlos A. (Засварлагч), Chen, Ding-Geng (Засварлагч)
Формат: Цахим Цахим ном
Хэл сонгох:англи
Хэвлэсэн: Cham : Springer Nature Switzerland : Imprint: Springer, 2024.
Хэвлэл:1st ed. 2024.
Цуврал:Emerging Topics in Statistics and Biostatistics,
Нөхцлүүд:
Онлайн хандалт:https://doi.org/10.1007/978-3-031-69622-0
Шошгууд: Шошго нэмэх
Шошго байхгүй, Энэхүү баримтыг шошголох эхний хүн болох!

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490 1 |a Emerging Topics in Statistics and Biostatistics,  |x 2524-7743 
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 |a 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. 
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