Statistical Models and Methods for Data Science

✍ Scribed by Leonardo Grilli; Monia Lupparelli; Carla Rampichini; Emilia Rocco; Maurizio Vichi

• Publisher: Springer International Publishing
• Year: 2023
• Tongue: English
• Leaves: 188
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Cover
Front Matter
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 Stabilization
A Random-Coefficients Analysis with a Multivariate Random-Coefficients Linear Model
Parsimonious Mixtures of Matrix-Variate Shifted Exponential Normal Distributions
Back Matter