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- W3136403453 abstract "The method of least absolute deviation provides a robust alternative to least squares, particularly when the data follow distributions that are non-normal and subject to outliers. While inference in least squares estimation is well understood, inferential procedures in the situation of least absolute deviation estimation have not been studied as extensively, particularly in the presence of autocorrelation. In this search, we study two alternative significance test procedures in least absolute deviation regression, along with two approaches used to correct for serial correlation. The study is based on a Monte Carlo simulation, and comparisons are made based on observed significance levels." @default.
- W3136403453 created "2021-03-29" @default.
- W3136403453 creator A5010233342 @default.
- W3136403453 date "2020-01-01" @default.
- W3136403453 modified "2023-09-26" @default.
- W3136403453 title "Statistical Inference for Least Absolute Deviation Regression with Autocorrelated Errors" @default.
- W3136403453 doi "https://doi.org/10.35741/issn.0258-2724.55.2.48" @default.
- W3136403453 hasPublicationYear "2020" @default.
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