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- W1998806985 abstract "We develop a small sample criterion (L1cAIC) for the selection of least absolute deviations regression models. In contrast to AIC (Akaike, 1973), L1cAIC provides an exactly unbiased estimator for the expected Kullback—Leibler information, assuming that the errors have a double exponential distribution and the model is not underfitted. In a Monte Carlo study, L1cAIC is found to perform much better than AIC and AICR (Ronchetti, 1985). A small sample criterion developed for normal least squares regression (cAIC, Hurvich and Tsai, 1988) is found to perform as well as L1cAIC. Further, cAIC is less computationally intensive than L1cAIC." @default.
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- W1998806985 date "1990-03-01" @default.
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- W1998806985 title "Model selection for least absolute deviations regression in small samples" @default.
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- W1998806985 doi "https://doi.org/10.1016/0167-7152(90)90065-f" @default.
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