nlControl: Control parameters for gradient search estimation

In Qtools: Utilities for Quantiles

Control parameters for gradient search estimation

Description

A list of parameters for controlling the fitting process.

Usage

nlControl(tol_ll = 1e-05, tol_theta = 0.001, check_theta = FALSE,
	step = NULL, beta = 0.5, gamma = 1.25, reset_step = FALSE,
	maxit = 1000, smooth = FALSE, omicron = 0.001, verbose = FALSE)

Arguments

tol_ll	tolerance expressed as relative change of the objective function.
tol_theta	tolerance expressed as relative change of the estimates.
check_theta	logical flag. If TRUE the algorithm performs a check on the change in the estimates in addition to the likelihood.
step	step size (default standard deviation of response).
beta	decreasing step factor for line search (0,1).
gamma	nondecreasing step factor for line search (>= 1).
reset_step	logical flag. If TRUE the step size is re-setted to the initial value at each iteration.
maxit	maximum number of iterations.
smooth	logical flag. If TRUE the standard loss function is replaced with a smooth approximation.
omicron	small constant for smoothing the loss function when using smooth = TRUE. See details.
verbose	logical flag.

Details

The optimization algorithm is along the lines of the gradient search algorithm (Bottai et al, 2015). If smooth = TRUE, the classical non-differentiable loss function is replaced with a smooth version (Chen and Wei, 2005).

Value

a list of control parameters.

Author(s)

Marco Geraci

References

Bottai M, Orsini N, Geraci M (2015). A Gradient Search Maximization Algorithm for the Asymmetric Laplace Likelihood, Journal of Statistical Computation and Simulation, 85(10), 1919-1925.

Chen C, Wei Y (2005). Computational issues for quantile regression. Sankhya: The Indian Journal of Statistics, 67(2), 399-417.

See Also

nlrq1

Qtools documentation built on Nov. 2, 2023, 6:11 p.m.