Flexible Techniques to Detect Typical Hidden Errors in Large Longitudinal Datasets

• Published in: Symmetry (Basel) Vol. 16; no. 5; p. 529
• Main Authors: Bruni, Renato, Daraio, Cinzia, Di Leo, Simone
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2024
• Subjects: Analysis; Artificial intelligence; big data; data quality; Datasets; Education parks; Education, Higher; Empirical analysis; Error correction; Error correction & detection; Higher education institutions; information processing; information reconstruction; longitudinal data sequences; Longitudinal studies; Methods; School facilities; Time series; Germany

The increasing availability of longitudinal data (repeated numerical observations of the same units at different times) requires the development of flexible techniques to automatically detect errors in such data. Besides standard types of errors, which can be treated with generic error correction te...