est.tpn: Parameter estimation for the tpn

In tpn: Truncated Positive Normal Model and Extensions

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)

tpn documentation built on Sept. 28, 2023, 1:06 a.m.