mse.fisher_mle: Mean squared error for fisher_mle

In likelihood.model: Likelihood-Based Statistical Inference in the Fisherian Tradition

Mean squared error for fisher_mle

Description

Computes MSE = Var + Bias^2 (scalar) or Vcov + bias %*% t(bias) (matrix). Under regularity conditions, asymptotic bias is zero, so MSE equals the variance-covariance matrix. When model is provided, uses Monte Carlo bias estimation via bias.fisher_mle().

Usage

## S3 method for class 'fisher_mle'
mse(x, theta = NULL, ..., model = NULL, n_sim = 1000)

Arguments

x	A fisher_mle object
theta	True parameter value (for simulation studies)
...	Additional arguments (ignored)
model	A likelihood model (optional, enables MC bias estimation)
n_sim	Number of MC replicates for bias estimation (default 1000)

Value

MSE matrix or scalar

likelihood.model documentation built on March 19, 2026, 9:07 a.m.