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- W4295693359 abstract "It has previously been shown that ordinary least squares can be used to estimate the coefficients of the single-index model under only mild conditions. However, the estimator is non-robust leading to poor estimates for some models. In this paper we propose a new sliced least-squares estimator that utilizes ideas from Sliced Inverse Regression. Slices with problematic observations that contribute to high variability in the estimator can easily be down-weighted to robustify the procedure. The estimator is simple to implement and can result in vast improvements for some models when compared to the usual least-squares approach. While the estimator was initially conceived with the single-index model in mind, we also show that multiple directions can be obtained, therefore providing another notable advantage of using slicing with least squares. Several simulation studies and a real data example are included, as well as some comparisons with some other recent methods." @default.
- W4295693359 created "2022-09-14" @default.
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- W4295693359 date "2022-09-10" @default.
- W4295693359 modified "2023-09-26" @default.
- W4295693359 title "Slice Weighted Average Regression" @default.
- W4295693359 doi "https://doi.org/10.48550/arxiv.2209.04616" @default.
- W4295693359 hasPublicationYear "2022" @default.
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