Applied Statistical Modeling and Data Mining

© 2024 by the authors; CC BY-NC-ND license

cross-validation; dataset shift; target shift; stratification; regression; noise models; nomenclature; taxonomy; noisy data; classification; family financial socialization; financial literacy; investment awareness; lack of self-control; saving behavior; Saudi Arabia; affine transformation; archaeology; flint (chert); multivariate statistics; pattern recognition; spectroscopy; security; crime prediction; police patrol routes; machine learning; artificial intelligence; energy efficient; federated learning; event-triggered communication; DAG-DLT; machine learning; respiratory failure; acute pesticide poisoning; logistic regression; random forests; long short-term memory; transportation system (TS); electrical vehicles (EVs); malware attack; offense–defense game; traffic flow; wireless sensor network (WSN); uncertainty; Cox processes; crime data; diffusion; emergency calls; spatio-temporal point processes; stochastic integro-differential equations; volatility; academic burnout syndrome; MBI-SS; internal structure; reliability and validity; ESSEM and bifactor model; utility measures; synthetic data; class imbalance; tabular data; academic burnout syndrome; Cuestionario Burnout Granada-University Students; CBG-USS; MBI-SS; nursing students; reliability and validity; highway; ETC gantry; topology dynamic generation; LightGBM; Bayesian model combination; ensemble learning; logic tree; logic regression; machine learning; simulated annealing; UK10K project; variable interactions; emotional contagion; improved susceptible-infectious-recovered model; two sources of infection; machine learning; long-term refuge scenarios