mses: Mean Squared Error
In austinragotzy/mathstat: Introduction to Mathematical Statistics R Package

Description Usage Arguments Details Value References Examples

As part of the Monte Carlo investigation of the relative efficency between estimators for finite sample sizes, this function calculates the sum of squared differences of the inputted sample observations against the value of theta0. The sum is divided by size of the sample n.

1	mses(x, theta0 = 0)

`x`	A numeric vector containing sample size n
`theta0`	Variance of sample x

The Monte Carlo investigation of relative efficency between estimators for finite sample sizes is covered in Section 10.3.4 on page 595.

The mean squared error of the inputted sample and value for theta.

Hogg, R., McKean, J., Craig, A. (2018) Introduction to Mathematical Statistics, 8th Ed. Boston: Pearson.

sample1 <- rcn(n = 30, eps = 0.20, sigma_c = 25)
sample2 <- rcn(n = 20, eps = 0.20, sigma_c = 25)

theta1 <- var(sample1)
theta2 <- var(sample2)

MSE1 <- mses(sample1, theta1)
MSE2 <- mses(sample2, theta2)