The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Hierarchical Spline Models for Conditional Quantiles and the Demand for Electricity
Abstract
Methods for estimating nonparametric models for conditional quantiles are suggested based on the regression quantile methods of Koenker and Bassett. Spline parameterizations of the conditional quantile functions are used. The methods are illustrated by estimating hierarchical models for household electricity demand using data from the Chicago metropolitan area. The empirical results show that lower quantiles of demand (“base-load”) vary only slightly across residential households. This variability is difficult to explain using household characteristics. However, upper quantiles of the demand distribution vary considerably and are systematically related to household characteristics and appliance ownership. The implications of analyzing mean demand behavior rather than various quantiles of the distribution of demand are also discussed.
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Citations
Cites
Spline Models for Observational Data
Wahba, G. (1990) · doi:10.1137/1.9781611970128 · Cited by 4,184
Bayes Estimates for the Linear Model
Lindley, D. V., & Smith, A. F. M. (1972) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1972.tb00885.x · Cited by 2,025
Metadata
- Authors
- Hendricks, W., & Koenker, R.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 1992-03
- Volume
- 87
- Issue
- 417
- Pages
- 58-68
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W2020023242
- Cited by
- 324
- References
- 2
- FWCI
- 1.87
- Type
- journal article
- Language
- en
- Open access
- closed
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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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