The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
Nonparametric Regressin with Correlated Errors
Open accessAbstract
Nonparametric regression techniques are often sensitive to the presence of correlation in the errors. The practical consequences of this sensitivity are explained, including the breakdown of several popular data-driven smoothing parameter selection methods. We review the existing literature in kernel regression, smoothing splines and wavelet regression under correlation, both for short-range and long-range dependence. Extensions to random design, higher dimensional models and adaptive estimation are discussed.
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Cited by
A survey of cross-validation procedures for model selection
Arlot, S., & Celisse, A. (2010) · Statistics Surveys · doi:10.1214/09-ss054 · Cited by 3,522
On the use of cross-validation for time series predictor evaluation
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A note on the validity of cross-validation for evaluating autoregressive time series prediction
Bergmeir, C., Hyndman, R. J., & Koo, B. (2018) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2017.11.003 · Cited by 743
Gradient boosting machine for modeling the energy consumption of commercial buildings
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Williams, A. H., Poole, B., Maheswaranathan, N., Dhawale, A. K., Fisher, T., Wilson, C. D., Brann, D. H., Trautmann, E. M., Ryu, S., Shusterman, R., et al. (2020) · Neuron · doi:10.1016/j.neuron.2019.10.020 · Cited by 102
Metadata
- Authors
- Opsomer, J., Wang, Y., & Yang, Y.
- Venue
- Statistical Science
- Publisher
- Institute of Mathematical Statistics
- Published
- 2001-05-01
- Volume
- 16
- Issue
- 2
- Pages
- 134-153
- ISSN
- 0883-4237 (ISSN-L)
- OpenAlex
- W2090373435
- Cited by
- 272
- References
- 43
- FWCI
- 10.24
- Type
- journal article
- Language
- en
- Open access
- bronze
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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