國立高雄大學圖資館 |

登入

回首頁

Statistical modeling and applicationsmultivariate, 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 applicationsedited 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.
Contained By:	Springer Nature eBook
標題:	Statistics.
電子資源:	https://doi.org/10.1007/978-3-031-69622-0
ISBN:	9783031696220$q(electronic bk.)

Statistical modeling and applicationsmultivariate, 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$q(electronic bk.)

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

182057
Statistics.

LC Class. No.: QA276

Dewey Class. No.: 519.54

Statistical modeling and applicationsmultivariate, heavy-tailed, skewed distributions and mixture modeling.Volume 2 /
LDR:03351nmm a2200337 a 4500 001 673492
003 DE-He213
005 20241218115505.0
006 m d
007 cr nn 008maaau
008 250422s2024 sz s 0 eng d
020 $a 9783031696220$q(electronic bk.)
020 $a 9783031696213$q(paper)
024 7 $a 10.1007/978-3-031-69622-0 $2 doi
035 $a 978-3-031-69622-0
040 $a GP $c GP
041 0 $a eng
050 4 $a QA276
072 7 $a PBT $2 bicssc
072 7 $a MAT029000 $2 bisacsh
072 7 $a PBT $2 thema
082 0 4 $a 519.54 $2 23
090 $a QA276 $b .S797 2024
245 0 0 $a Statistical modeling and applications $h [electronic resource] : $b multivariate, heavy-tailed, skewed distributions and mixture modeling. $n Volume 2 / $c edited by Carlos A. Coelho, Ding-Geng Chen.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2024.
300 $a xx, 250 p. : $b ill., digital ; $c 24 cm.
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.
650 0 $a Statistics. $3 182057
650 0 $a Mathematical models. $3 182479
650 2 4 $a Applied Statistics. $3 805583
650 2 4 $a Methodology of Data Collection and Processing. $3 921586
700 1 $a Coelho, Carlos A. $3 856976
700 1 $a Chen, Ding-Geng. $3 487343
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Emerging topics in statistics and biostatistics. $3 858970
856 4 0 $u https://doi.org/10.1007/978-3-031-69622-0
950 $a Mathematics and Statistics (SpringerNature-11649)