MLE_LambertW {LambertW} | R Documentation |
Maximum Likelihood Estimation (MLE) theta for Lambert W RV for Gaussian and student-t input.
MLE_LambertW(y, distname = c("normal"), theta.0 = IGMM(y)$theta, hessian=TRUE) ## Default S3 method: MLE_LambertW(y, distname = c("normal"), theta.0 = IGMM(y)$theta, hessian=TRUE)
y |
a numeric vector of real values. |
distname |
input distribution; default: "normal" , alternative "t" |
theta.0 |
starting value for numerical optimization; default: IGMM estimate. |
hessian |
return the (numerically obtained) hessian matrix at the optimum?; default: TRUE |
An object of class LWest
:
data |
the data |
theta.0 |
initial value |
theta |
MLE for theta |
logLH |
log-likelihood function (argument for the summary function to numerically calculate the Hessian) |
hessian |
Hessian matrix; used to calculate standard errors |
call |
function call |
message |
message from the optimization method. What kind of convergence? |
distname |
character string indicating the input distribution: "t" or "normal" . |
method |
Estimation method. Here "MLE" |
Georg M. Goerg
Goerg, G.M. (2009). “Lambert W Random Variables - A new class of skewed distribution functions”. Unpublished
data(AA) attach(AA) X=AA[AA$sex=="f",] y=X$bmi fit.ml=MLE_LambertW(y) summary(fit.ml) plot(fit.ml)