Change point analysis theory and application / Baisuo Jin and Jialiang Li.
| Author/creator | Jin, Baisuo |
| Other author | Li, Jialiang (Statistics professor |
| Format | Electronic |
| Edition | First edition. |
| Publication Info | Boca Raton, FL : CRC Press, 2026. |
| Description | pages cm |
| Supplemental Content | Full text available from eBooks on EBSCOhost |
| Subjects |
| Abstract | "Change point analysis is a crucial statistical technique for detecting structural breaks within datasets, applicable in diverse fields such as finance and weather forecasting. The authors of this book aim to consolidate recent advancements and broaden the scope beyond traditional time series applications to include biostatistics, longitudinal data analysis, high-dimensional data, and network analysis. The book introduces foundational concepts with practical data examples from literature, alongside discussions of related machine learning topics. Subsequent chapters focus on mathematical tools for single- and multiple-change point detection along with statistical inference issues, which provide rigorous proofs to enhance understanding but assume readers have foundational knowledge in graduate-level probability and statistics. The book also expands the discussion into threshold regression frameworks linked to subgroup identification in modern statistical learning and apply change point analysis to functional data and dynamic networks-areas not comprehensively covered elsewhere"-- Provided by publisher. |
| General note | Includes index. |
| Access restriction | Available only to authorized users. |
| Technical details | Mode of access: World Wide Web |
| Genre/form | Electronic books. |
| LCCN | 2025007258 |
| ISBN | 9781032649047 hardback |
| ISBN | 9781032657042 paperback |
| ISBN | ebook |