Robust optimal subsampling based on weighted asymmetric least squares
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Statistics, Probability and Uncertainty,Statistics and Probability
Link
https://link.springer.com/content/pdf/10.1007/s00362-023-01480-7.pdf
Reference40 articles.
1. Ai M, Wang F, Yu J, Zhang H (2021) Optimal subsampling for large-scale quantile regression. J Complexity 62:101512
2. Ai M, Yu J, Zhang H, Wang H (2021) Optimal subsampling algorithms for big data regressions. Stat Sin 31(2):749–772
3. Aigner D, Amemiya T, Poirier D (1976) On the estimation of production frontiers: maximum likelihood estimation of the parameters of a discontinuous density function. Int Econ Rev 17(2):377–396
4. Barry A, Oualkacha K, Charpentier A (2022) A new gee method to account for heteroscedasticity using asymmetric least-square regressions. J Appl Stat 49(14):3564–3590
5. Bowman A (1984) An alternative method of cross-validation for the smoothing of density estimates. Biometrika 71(2):353–360
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