This function computes the Least Squares Estimators (LSEs) of the shape and scale parameters of the Weibull distribution, based on the probability plot, from an i.i.d sample x. It also gives the sample \tilde{y} after using the logarithmic transformation (\tilde{y}=(\widetilde{shape})\ln(x/\widetilde{scale}), where \widetilde{shape} and \widetilde{scale} are the estimated shape and scale parameters).
1  LSEst(x)

x 
a numeric vector of data values. 
The elements of the numeric vector should be positive. The support of the Weibull distribution is R+*. These estimators are used by Liao and Shimokawa; they are based on the probability plot and symmetrical ranks.
A list containing the following elements:
eta 
the least squares estimator of the scale parameter of the Weibull distribution (\widetilde{scale}). 
beta 
the least squares estimator of the shape parameter of the Weibull distribution (\widetilde{shape}). 
y 
the pseudoobservations \tilde{y} after using the logarithmic transformation and the LSEs. 
Meryam KRIT
Liao M. and Shimokawa T., A new goodnessoffit test for typeI extremevalue and 2parameter Weibull distributions with estimated parameters, Journal of Statistical Computation and Simulation, 64 (1), 2348, 1999.
Krit M., Gaudoin O., Xie M. and Remy E., Simplified likelihood goodnessoffit tests for the Weibull distribution, Communications in Statistics  Simulation and Computation.
1 2 3 4 5 6 7 8 9 
Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.
Please suggest features or report bugs with the GitHub issue tracker.
All documentation is copyright its authors; we didn't write any of that.