Statistical Models and Learning Methods for Complex Data / edited by Giuseppe Giordano, Michele La Rocca, Marcella Niglio, Marialuisa Restaino, Maurizio Vichi.

Format:

Book

Author/Creator:

Giordano, Giuseppe.

Contributor:

La Rocca, Michele.

Niglio, Marcella.

Restaino, Marialuisa.

Vichi, Maurizio.

Series:

Studies in Classification, Data Analysis, and Knowledge Organization, 2198-3321

Language:

English

Subjects (All):

Mathematical statistics--Data processing.

Mathematical statistics.

Statistics.

Data mining.

Quantitative research.

Statistics and Computing.

Statistical Theory and Methods.

Applied Statistics.

Data Mining and Knowledge Discovery.

Data Analysis and Big Data.

Local Subjects:

Statistics and Computing.

Statistical Theory and Methods.

Applied Statistics.

Data Mining and Knowledge Discovery.

Data Analysis and Big Data.

Physical Description:

1 online resource (312 pages)

Edition:

1st ed. 2025.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2025.

Summary:

This book on statistical models and learning methods for complex data comprises a selection of peer-reviewed post-conference papers presented at the 14th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society (CLADAG 2023), held in Salerno, Italy, September 11–13, 2023. The contributions span a variety of topics, including different approaches to clustering and classification, multidimensional data analysis, panel data, social networks, time series, statistical inference, and mixture models. These methodologies are applied to a range of empirical domains such as economics, finance, hydrology, the social sciences, education, and sports. Organized biennially by international scientific committees, the CLADAG meetings advance methodological research in multivariate statistics, with a strong focus on data analysis and classification. They facilitate the exchange of ideas in these fields and promote the dissemination of concepts, numerical methods, algorithms, and computational and applied results. Chapter "Identification of misogynistic accounts on Twitter through Graph Convolutional Networks" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

Contents:

- Exploring latent evolving ability in test equating and its effects on final rankings

Hidden Markov and related discrete latent variable models An application to compositional data

An application of Natural Language Processing Analysis on TripAdvisor Reviews

Modelling football players field position via mixture of Gaussians with flexible weights

Estimation Issues in Multivariate Panel Data

Testing linearity in the single functional index model for dependent data

A multi-step approach for streamflow classification

Identification of misogynistic accounts on Twitter through Graph Convolutional Networks

Topic modeling of publication activity in Hungary and Poland in the fields of economics, finance, and business

Circular kernel classification with errors-in-variables

Classification Trees Applied to Time Lagged Data to Improve Quality in Official Statistics

Trimmed factorial k-means a clustering application to a cookies dataset_Farné and Camillo

Visualization of Proximity and Role-based Embeddings in a Regional Labour Flow Network

Bridging the Gap Investigating Correlation Clustering and Manifold Learning Connections

Improving Performance in Neural Networks by Dendrite-Activated Connection

Regression models with compositional regressors in case of structural zeros

Multi-Dimensional Robinson Dissimilarities

Composite selection criteria for the number of components of a finite mixture for ordinal data

Clustering of Italian higher education institutions based on a destination–specific approach

Analyzing Italian crime data using matrix-variate hidden Markov models.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

3-031-84702-4

9783031847028

OCLC:

1547906670