avar_mle {estimators}

MLE Asymptotic Variance

Description

Calculates the asymptotic variance (or variance - covariance matrix in the multidimensional case) of the MLE, given a specified family of distributions and the true parameter values.

Usage

avar_mle(distr, ...)

Arguments

distr

A subclass of Distribution. The distribution family assumed.

...

extra arguments.

Value

A named matrix. The asymptotic covariance matrix of the estimator.

See Also

avar, avar_me, avar_same

Examples

# -----------------------------------------------------
# Beta Distribution Example
# -----------------------------------------------------

# Simulation
set.seed(1)
shape1 <- 1
shape2 <- 2
D <- Beta(shape1, shape2)
x <- r(D)(100)

# Likelihood - The ll Functions

llbeta(x, shape1, shape2)
ll(x, c(shape1, shape2), D)
ll(x, c(shape1, shape2), "beta")

# Point Estimation - The e Functions

ebeta(x, type = "mle")
ebeta(x, type = "me")
ebeta(x, type = "same")

mle(x, D)
me(x, D)
same(x, D)

estim(x, D, type = "mle")

# Asymptotic Variance - The v Functions

vbeta(shape1, shape2, type = "mle")
vbeta(shape1, shape2, type = "me")
vbeta(shape1, shape2, type = "same")

avar_mle(D)
avar_me(D)
avar_same(D)

avar(D, type = "mle")