| est.tpn {tpn} | R Documentation |
Parameter estimation for the tpn
Description
Perform the parameter estimation for the truncated positive normal (tpn) discussed in Gomez et al. (2018) based on maximum likelihood estimation. Estimated errors are computed based on the hessian matrix.
Usage
est.tpn(y)
Arguments
y |
the response vector. All the values must be positive. |
Details
A variable have tpn distribution with parameters \sigma>0 and \lambda \in R if its probability density
function can be written as
f(y; \sigma, \lambda, q) = \frac{\phi\left(\frac{y}{\sigma}-\lambda\right)}{\sigma \Phi(\lambda)}, y>0,
where \phi(\cdot) and \Phi(\cdot) denote the density and cumultative distribution functions for the standard normal distribution.
Value
A list with the following components
estimate |
A matrix with the estimates and standard errors |
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., 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=rtpn(n=100,sigma=10,lambda=1)
est.tpn(y)