We present an estimating framework for quantile regression where the usual L1-norm objective function is replaced by its smooth parametric approximation. An exact path-following algorithm is derived, leading to the well-known ‘basic’ solutions interpolating exactly a number of observations equal to the number of parameters being estimated. We discuss briefly possible practical implications of the proposed approach, such as early stopping for large data sets, confidence intervals, and additional topics for future research.

Muggeo, V., Sciandra, M., Augugliaro, L. (2012). Quantile regression via iterative least squares computations. JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, 82, 1557-1569 [10.1080/00949655.2011.583650].

Quantile regression via iterative least squares computations

MUGGEO, Vito Michele Rosario;SCIANDRA, Mariangela;AUGUGLIARO, Luigi
2012-01-01

Abstract

We present an estimating framework for quantile regression where the usual L1-norm objective function is replaced by its smooth parametric approximation. An exact path-following algorithm is derived, leading to the well-known ‘basic’ solutions interpolating exactly a number of observations equal to the number of parameters being estimated. We discuss briefly possible practical implications of the proposed approach, such as early stopping for large data sets, confidence intervals, and additional topics for future research.
2012
Settore SECS-S/01 - Statistica
Muggeo, V., Sciandra, M., Augugliaro, L. (2012). Quantile regression via iterative least squares computations. JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, 82, 1557-1569 [10.1080/00949655.2011.583650].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/62348
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