nmixp {PivotalP}R Documentation

Prediction future points from mixture normal distribution

Description

Construct a prediction point for future observations from mixture normal distribution. Generic method is print.

Usage

nmixp(data, s, n, a ,parameters, conf=0.95)

## S3 method for class 'nmixp'
print(x, ...)

Arguments

data

A numeric vector.

s

A numeric value the order of prediction point.

n

A numeric vector for the size of all data.

a

A numeric value of mixing proportion.

parameters

A numeric vector of the parameter of distributions

conf

Confidence level for the test.

x

An object of class "nmixp".

...

Further argument to be passed to generic function

Details

Prediction of future observations if the data follows a mixture of two normal distributions

Value

nmixp returns an object of class "nmixp", a list with the following components:

interval

the prediction interval.

lower

the lower bound of the interval.

upper

the upper bound of the interval.

r

the length of the data.

s

the order of the next observation.

n

the length of all the data.

parameters

the parameter estimate.

Generic function:

print

The print of a "nmixp" object shows the prediction point(s) for the future observation(s).

Author(s)

O. M. Khaled, K. S. Khalil and M. H. Harby.

References

H. M. Barakat, Magdy E. El-Adll, Amany E. Aly (2014), Prediction intervals of future observations for a sample random size from any continuous distribution. Mathematics and Computers in Simulation, volume 97, 1-13.

O. M. Khaled, K. S. Khalil and M. H. Harby (2023), PREDICTING FUTURE DATA FROM GAMMA-MIXTURE AND BETA-MIXTURE DISTRIBUTIONS AND APPLICATION TO THE RECOVERY RATE OF COVID-19. Advances and Applications in Statistics (AAIS), OCT, 2023.

See Also

PredictionR.

Examples

# prediction interval and point for the next observations based on mixture normal distribution
#
set.seed(123)
x1 <- 0.5*rnorm(7, 4, 2)+0.5*rnorm(7, 1, 3)
nmixp(x1, 8, 10,0.5,c(4,2,1,3),conf=0.95)

[Package PivotalP version 0.1.2 Index]