Statistical Models and Methods for Data Science

This book focuses on methods and models in classification and data analysis and presents real-world applications at the interface with data science. Numerous topics are covered, ranging from statistical inference and modelling to clustering and factorial methods, and from directional data analysis t...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Grilli, Leonardo (Editor), Lupparelli, Monia (Editor), Rampichini, Carla (Editor), Rocco, Emilia (Editor), Vichi, Maurizio (Editor)
Formato: Electrónico eBook
Lenguaje:inglés
Publicado: Cham : Springer International Publishing : Imprint: Springer, 2023.
Edición:1st ed. 2023.
Colección:Studies in Classification, Data Analysis, and Knowledge Organization,
Materias:
Acceso en línea:https://doi.org/10.1007/978-3-031-30164-3
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!

MARC

LEADER 00000nam a22000005i 4500
001 978-3-031-30164-3
003 DE-He213
005 20260810154756.0
007 cr nn 008mamaa
008 230724s2023 sz | o |||| 0|eng d
020 |a 9783031301643  |9 978-3-031-30164-3 
024 7 |a 10.1007/978-3-031-30164-3  |2 doi 
050 4 |a QA276.4-.45 
072 7 |a PBT  |2 bicssc 
072 7 |a UFM  |2 bicssc 
072 7 |a COM077000  |2 bisacsh 
072 7 |a PBT  |2 thema 
072 7 |a UFM  |2 thema 
082 0 4 |a 519.5  |2 23 
245 1 0 |a Statistical Models and Methods for Data Science  |h [electronic resource] /  |c edited by Leonardo Grilli, Monia Lupparelli, Carla Rampichini, Emilia Rocco, Maurizio Vichi. 
250 |a 1st ed. 2023. 
264 1 |a Cham :  |b Springer International Publishing :  |b Imprint: Springer,  |c 2023. 
300 |a 1 online resource (VIII, 188 p. 32 illus.).  |b 19 illus. in color. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
347 |a text file  |b PDF  |2 rda 
490 1 |a Studies in Classification, Data Analysis, and Knowledge Organization,  |x 2198-3321 
505 0 |a Clustering financial time series by dependency -- The Homogeneity Index as a Measure of Interrater Agreement for Ratings on a Nominal Scale -- Hierarchical clustering of income data based on share densities -- Optimal Coding of High Cardinality Categorical Data in Machine Learning -- Bayesian Multivariate Analysis of Mixed data -- Marginals matrix under a generalized Mallows model based on the power divergence -- Time series clustering based on forecast distributions: an empirical analysis on production indices for construction -- Partial Reconstruction of Measures from Halfspace Depth -- Posterior Predictive Assessment of IRT Models via the Hellinger Distance: A Simulation Study -- Shapley Lorenz values for credit risk management -- A study of lack-of-fit diagnostics for models fit to cross-classified binary variables -- Robust Response Transformations for Generalized Additive Models via Additivity and Variance Stabilisation -- A Random-Coefficients Analysis with a Multivariate Random-Coefficients Linear Model -- Parsimonious mixtures of matrix-variate shifted exponential normal distributions. 
520 |a This book focuses on methods and models in classification and data analysis and presents real-world applications at the interface with data science. Numerous topics are covered, ranging from statistical inference and modelling to clustering and factorial methods, and from directional data analysis to time series analysis and small area estimation. The applications deal with new developments in a variety of fields, including medicine, finance, engineering, marketing, and cyber risk. The contents comprise selected and peer-reviewed contributions presented at the 13th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society, CLADAG 2021, held (online) in Florence, Italy, on September 9–11, 2021. CLADAG promotes advanced methodological research in multivariate statistics with a special focus on data analysis and classification, and supports the exchange and dissemination of ideas, methodological concepts, numerical methods, algorithms, and computational and applied results at the interface between classification and data science. 
588 |a Print version record. 
650 0 |a Mathematical statistics  |x Data processing. 
650 0 |a Quantitative research. 
650 0 |a Machine learning. 
650 0 |a Statistics . 
650 0 |a Artificial intelligence  |x Data processing. 
650 1 4 |a Statistics and Computing. 
650 2 4 |a Data Analysis and Big Data. 
650 2 4 |a Statistical Learning. 
650 2 4 |a Statistical Theory and Methods. 
650 2 4 |a Applied Statistics. 
650 2 4 |a Data Science. 
700 1 |a Grilli, Leonardo.  |e editor.  |0 (orcid)0000-0002-3886-7705  |1 https://orcid.org/0000-0002-3886-7705  |4 edt  |4 http://id.loc.gov/vocabulary/relators/edt 
700 1 |a Lupparelli, Monia.  |e editor.  |0 (orcid)0000-0002-2933-4691  |1 https://orcid.org/0000-0002-2933-4691  |4 edt  |4 http://id.loc.gov/vocabulary/relators/edt 
700 1 |a Rampichini, Carla.  |e editor.  |0 (orcid)0000-0002-8519-083X  |1 https://orcid.org/0000-0002-8519-083X  |4 edt  |4 http://id.loc.gov/vocabulary/relators/edt 
700 1 |a Rocco, Emilia.  |e editor.  |0 (orcid)0000-0003-4267-0734  |1 https://orcid.org/0000-0003-4267-0734  |4 edt  |4 http://id.loc.gov/vocabulary/relators/edt 
700 1 |a Vichi, Maurizio.  |e editor.  |0 (orcid)0000-0002-3876-444X  |1 https://orcid.org/0000-0002-3876-444X  |4 edt  |4 http://id.loc.gov/vocabulary/relators/edt 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer Nature eBook 
776 0 8 |i Printed edition:  |z 9783031301636 
776 0 8 |i Printed edition:  |z 9783031301650 
830 0 |a Studies in Classification, Data Analysis, and Knowledge Organization,  |x 2198-3321 
856 4 0 |u https://doi.org/10.1007/978-3-031-30164-3 
912 |a ZDB-2-SMA 
912 |a ZDB-2-SXMS 
912 |a ZDB-2-SXPE 
950 |a Mathematics and Statistics (SpringerNature-11649) 
950 |a Mathematics and Statistics (R0) (SpringerNature-43713) 
950 |a Springer Nature Proceedings excluding Computer Science (SpringerNature-85345)