Abstract

Abstract In this article, we are concerned with least absolute deviation (LAD) estimation applied to linear regression and linear time series models. LAD estimation is widely used in statistical modeling and analysis. Compared to classical least squares (LS), LAD is a robust estimation procedure and is typically more efficient when handling heavy‐tailed data. We provide a selective overview on its developments in linear regression and time series models. This overview includes a description of a basic method for establishing the asymptotic properties of the LAD estimator for finite variance autoregressive moving average (ARMA) models. Efficiency of LAD estimation relative to LS estimation as well as computational issues are also discussed.

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