DEEVD (version 1.2.3)

mse: Calculate Mean Square Error( MSE) by using Extreme value distributions

Description

This function calculates the mean squared error (MSE) by using user specified kernel. But distribution of vector should be Exponential, Gamma, Gumbel, Frechet or Weibull. Any other choice of distribution will result NaN. This function is simillar to function mse in DELTD, but here more distributions are available for distribution vector.

Usage

mse(kernel, type)

Arguments

kernel

type of kernel which is to be used

type

mention distribution of vector.If exponential distribution then use "Exp". If use gamma distribution then type "Gamma".If Gumbel distribution is used with scale=1, then use "Gumbel". If Weibull distribution then use "Weibull". If use Weibull distribution with scale = 1 then use "Weibull". If use Frechet distribution with scale=1 and shape=1 then use "Frechet".

Value

Mean Squared Error (MSE)

References

• Salha, R. B., El Shekh Ahmed, H. I., & Alhoubi, I. M. 2014. Hazard Rate Function Estimation Using Weibull Kernel. Open Journal of Statistics 4 (08), 650-661.

• Khan, J. A., & Akbar, A. 2021. Density Estimation Using Gumbel Kernel Estimator. Open Journal of Statistics 11 (2), 319-328.

Examples

# NOT RUN {
y <- rexp(100, 1)
xx <- seq(min(y) + 0.05, max(y), length = 500)
h <- 2
gr <- Gumbel(x = xx, y = y, k = 200, h = h)
mse(kernel = gr, type = "Exp")
## if distribution is other than mentioned \code{type} is used then NaN will be produced.
# }
# NOT RUN {
mse(kernel = gr, type ="Beta")
# }