Comparison of loss functions for linear regression

This paper addresses selection of the loss function for regression problems with finite data. It is well-known (under standard regression formulation) that for a known noise density there exist an optimal loss function under an asymptotic setting (large number of samples), i.e. squared loss is optimal for Gaussian noise density. However, in real-life applications the noise density is unknown and the number of training samples is finite. For such practical situations, we suggest using Vapnik's /spl epsiv/-insensitive loss function. We use practical method for setting the value of /spl epsiv/ as a function of known number of samples and (known or estimated) noise variance (V. Cherkassky and Y. Ma, (2004), (2002)). We consider commonly used noise densities (such as Gaussian, Uniform and Laplacian noise). Empirical comparisons for several representative linear regression problems indicate that Vapnik's /spl epsiv/-insensitive loss yields more robust performance and improved prediction accuracy, in comparison with squared loss and least-modulus loss, especially for noisy high-dimensional data sets.