choose.fts: Choose a distribution in the flexible truncated positive...

In tpn: Truncated Positive Normal Model and Extensions

Choose a distribution in the flexible truncated positive class of models

Description

Provide model selection for a given data set in the flexible truncated positive class of models

Usage

choose.fts(y, criteria = "AIC")

Arguments

y	positive vector of responses
criteria	model criteria for the selection: AIC (default) or BIC.

Details

The function fits the truncated positive normal, truncated positive laplace, truncated positive Cauchy and truncated positive logistic models and select the model which provides the lower criteria (AIC or BIC).

Value

A list with the following components

AIC	a vector with the AIC for the different truncated positive fitted models: normal, laplace, cauchy and logistic.
selected	the selected model
estimate	the estimated for sigma and lambda and the respective standard errors (s.e.)
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.

Author(s)

Gallardo, D.I., Gomez, H.J. and Gomez, Y.M.

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")
choose.fts(y)

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