est.fts {tpn} | R Documentation |
Parameter estimation for the ftp class of distributions
Description
Perform the parameter estimation for the Flexible truncated positive (fts) class discussed in Gomez et al. (2022) based on maximum likelihood estimation. Estimated errors are computed based on the hessian matrix.
Usage
est.fts(y, dist="norm")
Arguments
y |
the response vector. All the values must be positive. |
dist |
standard symmetrical distribution. Avaliable options: norm (default), logis, cauchy and laplace. |
Details
A variable has fts distribution with parameters \sigma>0
and \lambda \in
R if its probability density
function can be written as
f(y; \sigma, \lambda, q) = \frac{g_0(\frac{y}{\sigma}-\lambda)}{\sigma G_0(\lambda)}, y>0,
where g_0(\cdot)
and G_0(\cdot)
denote the pdf and cdf for the specified distribution.
The case where g_0(\cdot)
and G_0(\cdot)
are from the standard normal model is known as the truncated positive normal model discussed
in Gomez et al. (2018).
Value
A list with the following components
estimate |
A matrix with the estimates and standard errors |
dist |
distribution specified |
conv |
the code related to the convergence for the optim function. 0 if the convergence was attached. |
logLik |
log-likelihood function evaluated in the estimated parameters. |
AIC |
Akaike's criterion. |
BIC |
Schwartz's criterion. |
Note
A warning is presented if the estimated hessian matrix is not invertible.
Author(s)
Gallardo, D.I. and Gomez, H.J.
References
Gomez, H.J., Gomez, H.W., Santoro, K.I., Venegas, O., Gallardo, D.I. (2022). A Family of Truncation Positive Distributions. Submitted.
Gomez, H.J., Olmos, N.M., Varela, H., Bolfarine, H. (2018). Inference for a truncated positive normal distribution. Applied Mathemetical Journal of Chinese Universities, 33, 163-176.
Examples
set.seed(2021)
y=rfts(n=100,sigma=10,lambda=1,dist="logis")
est.fts(y,dist="logis")