parInfer.WM: WM-estimate Inference

In nlr: Nonlinear Regression Modelling using Robust Methods

Description

Parameter inference for weighted M-estimate. WM-estimate is based on minimizing the robustified form of likelihood, simultanously over nonlinear function parameter and variance model parameters the the covariance of parameter, the estimate is assymptotically normal (See Lim et al.2010) with given covariance matric which compute for sample by the function.

Usage

parInfer.WM(object, confidence = 0.95)

Arguments

object	nl.fitt.rgn object of WM-fitt generated by nl.robhetroWM function.
confidence	Confidence probability.

Details

Compute covariance matrix and confidence interval for nonlinear model function parameter and nonlinear variance model parameters.

Value

covmat:	Covariance matrix of nonlinear model function parameters.
covtau	Covariance matrix of nonlinear variance model parameters.
parstdev	Standard deviation of nonlinear model function parameter. It is square root of diagonal of covmat.
CI	Confidence interval for nonlinear model function parameter.

Note

ParInfer method of nl.fitt.rgn call this function automatically, so user might not call it directly.

This function call by nlr, for compatibility it is better to call from nlr rather than directly by user.

Author(s)

Hossein Riazoshams, May 2014. Email: [e-mail] URL http://www.riazoshams.com/nlr/

References

Lim, C., Sen, P. K., Peddada, S. D. (2010). Statistical inference in nonlinear regression under heteroscedasticity. Sankhya B 72:202-218.

See Also

nl.fitt.rgn, nl.robhetroWM

Examples

## The function is currently defined as
"parInfer.WM"

nlr documentation built on July 31, 2019, 5:09 p.m.