| Koenker R. and Park B. J., An interior point algorithm for nonlinear quantile regression, Journal of Econometrics, vol.71, pp.265-283, 1996. |
....a quantile regression algorithm that operates by transferring optimization of the nondifferentiable objective function to a quadratic function. It is conceptually simple and easy to code, and our numerical tests suggest that it is computationally competitive with the interior point algorithm of Koenker and Park (1996). Key words and phrases: L 1 regression, majorization, EM algorithm, Gauss Newton method. OPTIMIZATION TRANSFER FOR QUANTILE REGRESSION 1 1 Introduction The fact that the median 1=2 of a random variable Y minimizes the expectation f( E (jY Gamma j) is well known among theoretical ....
....minimizer fi, then lim ffl 0 fi ffl = fi. OPTIMIZATION TRANSFER FOR QUANTILE REGRESSION 13 5 Numerical Results This section summarizes the results of several numerical tests of the optimization transfer algorithm. As a benchmark for comparison, we also test the interior point algorithm of Koenker and Park (1996). Their method is a primal dual algorithm, with a dual loop nested within the main primal loop. As recommended by Koenker and Park, we take two dual iterations for each primal iteration and declare convergence whenever the change jg(fi k ) Gamma g(fi k 1 )j in the value of the objective ....
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Koenker, R., and Park, B. J. (1996), "An interior point algorithm for nonlinear quantile regression," Journal of Econometrics, 71, 265--283.
No context found.
Koenker R. and Park B. J., An interior point algorithm for nonlinear quantile regression, Journal of Econometrics, vol.71, pp.265-283, 1996.
No context found.
R. Koenker, B. J. Park, An Interior Point Algorithm for Nonlinear Quantile Regression, Journal of Econometrics, 71, 1997, 265-283.
No context found.
Koenker, R. and Park, B.J. (1994). An Interior Point Algorithm for Nonlinear Quantile Regression, Journal of Econometrics, 71(1-2): 265-283.
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