innovations: Family of Innovations Process for Fitting a INARS(1) model

Description Usage Value Author(s) References Examples

Description

Provide the current available distributions that can be used as an innovation process in a fit of the INARS(1) model.

Usage

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SDL()

SK()

DLOG()

Value

The above functions return a list of the following components:

d

Probability mass function function(z, mu, disp).

r

A random generator function(n, mu, disp).

The parameters associated with these functions are:

These functions are used to define the innovation process in the inars() fit.

Author(s)

Rodrigo M. R. Medeiros <rodrigo.matheus@live.com>

References

Skellam, J. G. (1946). The frequency distribution of the difference between two Poisson variates belonging to different populations. Journal of the Royal Statistical Society: Series A, 109, 296.

Andersson, J., & Karlis, D. (2014). A parametric time series model with covariates for integers in Z. Statistical Modelling, 14, 135–156.

Chakraborty, S., & Chakravarty, D. (2016). A new discrete probability distribution with integer support on (–infty, infty). Communications in Statistics-Theory and Methods, 45, 492–505.

Medeiros, R.M.R., Bourguignon M. (2021) A useful regression model for integers on Z with application to quality of life in prisons. Submitted.

Examples

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sdl <- SDL()
skellam <- SK()
dlog <- DLOG()

zvals <- -10:10

## SDL
barplot(sdl$d(zvals, -2, 4), names.arg = zvals)
barplot(sdl$d(zvals, 0, 4), names.arg = zvals)
barplot(sdl$d(zvals, 2, 4), names.arg = zvals)

## Skellam
barplot(skellam$d(zvals, -2, 4), names.arg = zvals)
barplot(skellam$d(zvals, 0, 4), names.arg = zvals)
barplot(skellam$d(zvals, 2, 4), names.arg = zvals)

## Discret logistic
barplot(dlog$d(zvals, -2, 1), names.arg = zvals)
barplot(dlog$d(zvals, 0, 1), names.arg = zvals)
barplot(dlog$d(zvals, 2, 1), names.arg = zvals)

Rodrigo-sgj/inars documentation built on March 15, 2021, 11:52 a.m.