The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Asymptotics for Least Absolute Deviation Regression Estimators
Abstract
The LAD estimator of the vector parameter in a linear regression is defined by minimizing the sum of the absolute values of the residuals. This paper provides a direct proof of asymptotic normality for the LAD estimator. The main theorem assumes deterministic carriers. The extension to random carriers includes the case of autoregressions whose error terms have finite second moments. For a first-order autoregression with Cauchy errors the LAD estimator is shown to converge at a 1/n rate.
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Cites
Cox’s regression model for counting processes: A large sample study
Andersen, P. K., & Gill, R. D. (1982) · The Annals of Statistics · doi:10.1214/aos/1176345976 · Cited by 4,419
Cited by
Quantile cointegrating regression
Xiao, Z. (2009) · Journal of Econometrics · doi:10.1016/j.jeconom.2008.12.005 · Cited by 431
Metadata
- Authors
- Pollard, D.
- Venue
- Econometric Theory
- Publisher
- Cambridge University Press (CUP)
- Published
- 1991-06
- Volume
- 7
- Issue
- 2
- Pages
- 186-199
- ISSN
- 0266-4666 (ISSN-L)
- 1469-4360
- OpenAlex
- W2027123933
- Cited by
- 646
- References
- 1
- FWCI
- 7.57
- 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).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
