Point Estimation of the Parameters of Piecewise Regression Models
Abstract
Two methods of fitting piecewise multiple regression models are presented. One, based on dynamic programming, yields maximum‐likelihood estimators and is suitable for sequences of moderate length. A second, hierarchical, procedure yields approximations to the maximum‐likelihood estimators and is suitable for very long sequences of data. Both methods have computational requirements that are linear in the number of segments.
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When the recursive hypothesis test method is carried out using a semihierarchical splitting algorithm, the power of changepoint detection can be comparable to that of optimal algorithms.
“Second, MW05 noted that when the recursive hypothesis test method is carried out using a semihierarchical splitting algorithm (Hawkins 1976), the power of changepoint detection can be comparable to that of optimal algorithms.”
From Homogenization of Temperature Series via Pairwise Comparisons (2009) · cites this paper as Hawkins 1976
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Self-citation
- Authors
- Hawkins, D. M.
- Affiliations
- University of Minnesota (US)
- Corresponding author
- Hawkins, Douglas M.
- ORCID
- Hawkins, Douglas M.
- Venue
- Applied Statistics
- Publisher
- JSTOR
- Published
- 1976
- Volume
- 25
- Issue
- 1
- Pages
- 51
- ISSN
- 0035-9254 (ISSN-L)
- OpenAlex
- W2796286841
- Cited by
- 103
- References
- 0
- Citations by year
- 2023: 1 · 2022: 1 · 2021: 6 · 2020: 5 · 2019: 2 · 2018: 6 · 2017: 3 · 2016: 5 · 2015: 3 · 2014: 2 · 2013: 5 · 2012: 5
- FWCI
- 0.64
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of the Royal Statistical Society Series C (Applied Statistics) (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Applied Mathematics › Statistical and numerical algorithms
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Keywords
- piecewise linear regression, break point estimation, multiple regression model, maximum likelihood estimation, dynamic programming, linear computational complexity
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