Applied Statistical Modeling and Data Mining (Hardcover)

Jose Antonio Saez Munoz

ISBN 10: 3725811059 / ISBN 13: 9783725811052

Published by Mdpi AG, 2024

About this Item

Hardcover. 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. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Seller Inventory # 9783725811052

Bibliographic Details

• Title: Applied Statistical Modeling and Data Mining (Hardcover)
• Publisher: Mdpi AG
• Publication Date: 2024
• Language: English
• ISBN 10: 3725811059
• ISBN 13: 9783725811052
• Binding: Hardcover
• Condition: new

About this title

Synopsis

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.