Improving predictive inference under covariate shift by weighting the log-likelihood function
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Samek et al. proposed an approach to transfer information about nonstationarities in the data to reduce the shift between subjects, and verified its performance in MI BCIs.
“Samek et al. [27] proposed an approach to transfer information about nonstationarities in the data to reduce the shift between subjects, and verified its performance in MI BCIs.”
From Transfer Learning for Brain–Computer Interfaces: A Euclidean Space Data Alignment Approach (2020) · cites this paper as [27]
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“To train models robust to subpopulation shifts, we test the following approaches, both of which utilize training domain annotations: • Reweighting (Shimodaira, 2000).”
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- Authors
- Shimodaira, H.
- Affiliations
- The Institute of Statistical Mathematics (JP)
- Corresponding author
- Shimodaira, Hidetoshi
- Venue
- Journal of Statistical Planning and Inference
- Publisher
- Elsevier BV
- Published
- 2000-10
- Volume
- 90
- Issue
- 2
- Pages
- 227-244
- ISSN
- 0378-3758 (ISSN-L)
- OpenAlex
- W2034368206
- Cited by
- 1,678
- References
- 2
- Citations by year
- 2026: 89 · 2025: 97 · 2024: 102 · 2023: 125 · 2022: 128 · 2021: 201 · 2020: 170 · 2019: 126 · 2018: 108 · 2017: 71 · 2016: 60 · 2015: 52 · 2014: 66 · 2013: 49 · 2012: 50
- FWCI
- 0.89
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Statistical Planning and Inference (publishedVersion)
- Tokyo Tech Research Repository (Tokyo Institute of Technology) (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Survey Sampling and Estimation Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Keywords
- covariate shift, predictive density, quasi-maximum likelihood estimation, sample surveys, importance sampling, Kullback-Leibler loss, information criteria, Bayesian predictive density, model misspecification, polynomial regression
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