Applied Statistical Modeling and Data Mining
Materialtyp:
ArtikelUtgivningsinformation: MDPI - Multidisciplinary Digital Publishing Institute 2024Beskrivning: 1 electronic resource (354 p.)Innehållstyp: - text
- computer
- online resource
- 9783725811052
- 9783725811069
- Mathematics and Science
- Mathematics
- Applied mathematics
- Bayesian model combination
- CBG-USS
- Cox processes
- Cuestionario Burnout Granada-University Students
- DAG-DLT
- ESSEM and bifactor model
- ETC gantry
- LightGBM
- MBI-SS
- Saudi Arabia
- UK10K project
- academic burnout syndrome
- acute pesticide poisoning
- affine transformation
- archaeology
- artificial intelligence
- class imbalance
- classification
- crime data
- crime prediction
- cross-validation
- dataset shift
- diffusion
- electrical vehicles (EVs)
- emergency calls
- emotional contagion
- energy efficient
- ensemble learning
- event-triggered communication
- family financial socialization
- federated learning
- financial literacy
- flint (chert)
- highway
- improved susceptible-infectious-recovered model
- internal structure
- investment awareness
- lack of self-control
- logic regression
- logic tree
- logistic regression
- long short-term memory
- long-term refuge scenarios
- machine learning
- malware attack
- multivariate statistics
- noise models
- noisy data
- nomenclature
- nursing students
- offense–defense game
- pattern recognition
- police patrol routes
- random forests
- regression
- reli
Open Access Unrestricted online access star
This reprint comprises a compilation of 15 insightful research papers published in the Special Issue "Applied Statistical Modeling and Data Mining" within the Mathematics journal. Spanning multiple domains at the intersection of statistics, machine learning, and real-world applications, the collection presents diverse perspectives and innovative approaches in the field of applied statistical modeling and data mining. These selected papers explore diverse and important topics, including predictive modeling in healthcare, cybersecurity in transportation systems, data quality assessment, emergency call modeling, and innovative crime prevention strategies, among others. By covering a wide spectrum of subjects, this reprint serves as a valuable resource for researchers, academics, and practitioners interested in addressing contemporary challenges across diverse domains through statistical modeling, data mining, and machine learning applications.
Creative Commons Licence cc by-nc-nd cc https://creativecommons.org/licenses/by-nc-nd/4.0/
eng
Freely available e-book