est.btpn | R Documentation |
Perform the parameter estimation for the bimodal truncated positive normal (btpn) discussed in Gomez et al. (2022). Estimated errors are computed based on the hessian matrix.
est.btpn(y)
y |
the response vector. All the values must be positive. |
A variable have btpn distribution with parameters \sigma>0, \lambda \in
R and \eta \in
R if its probability density
function can be written as
f(y; \sigma, \lambda, q) = \frac{\phi\left(\frac{x}{\sigma(1+\epsilon)}+\lambda\right)}{2\sigma\Phi(\lambda)}, y<0,
and
f(y; \sigma, \lambda, q) = \frac{\phi\left(\frac{x}{\sigma(1-\epsilon)}-\lambda\right)}{2\sigma\Phi(\lambda)}, y\geq 0,
where \epsilon=\eta/\sqrt{1+\eta^2}
and \phi(\cdot)
and \Phi(\cdot)
denote the probability density function and the cumulative distribution
function for the standard normal distribution, respectively.
A list with the following components
estimate |
A matrix with the estimates and standard errors |
iter |
Iterations in which the convergence were attached. |
logLik |
log-likelihood function evaluated in the estimated parameters. |
AIC |
Akaike's criterion. |
BIC |
Schwartz's criterion. |
A warning is presented if the estimated hessian matrix is not invertible.
Gallardo, D.I., Gomez, H.J. and Gomez, Y.M.
Gomez, H.J., Caimanque, W., Gomez, Y.M., Magalhaes, T.M., Concha, M., Gallardo, D.I. (2022) Bimodal Truncation Positive Normal Distribution. Symmetry, 14, 665.
set.seed(2021)
y=rbtpn(n=100,sigma=10,lambda=1,eta=1.5)
est.btpn(y)
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