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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2023.11
- 수록면
- 1,013 - 1,030 (18page)
- DOI
- 10.7465/jkdi.2023.34.6.1013
이용수
초록· 키워드
오류제보하기
Various fields generate vast amounts of data that exhibit changes across time, space, and attributes. Detecting and comprehending these changes is crucial for interpreting real-world phenomena and making predictions, contributing to informed decision making. Change point detection is an analytical approach used to identify and characterize shifts in time series and spatial data. It establishes a basis for predicting patterns linked to detected change points, which often signify shifts in distribution, mean, variance, or correlation. These insights span domains such as nature, particularly as data volume and complexity rise, posing challenges in extracting meaningful insights. It has applications across sectors like finance and manufacturing. Penalization methods manage model complexity by adding penalties to coefficients, promoting sparsity and selecting pertinent features. The Adaptive lasso adapts weights for each variable, enhancing model accuracy. This paper delves into investigating weighted penalty methods for change point detection, aiming to validate their effectiveness through examples and discussions, contributing to enhanced data analysis and interpretation.
목차
Abstract
1. Introduction
2. Review on adaptive Lasso
3. Adaptive Lasso in regression spline estimation
4. Simulation studies
5. Data analysis
6. Conclusion
References
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