% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/core-fisher_mle.R
\name{mse.fisher_mle}
\alias{mse.fisher_mle}
\title{Mean squared error for fisher_mle}
\usage{
\method{mse}{fisher_mle}(x, theta = NULL, ..., model = NULL, n_sim = 1000)
}
\arguments{
\item{x}{A fisher_mle object}

\item{theta}{True parameter value (for simulation studies)}

\item{...}{Additional arguments (ignored)}

\item{model}{A likelihood model (optional, enables MC bias estimation)}

\item{n_sim}{Number of MC replicates for bias estimation (default 1000)}
}
\value{
MSE matrix or scalar
}
\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 \code{model} is provided, uses Monte Carlo
bias estimation via \code{\link[=bias.fisher_mle]{bias.fisher_mle()}}.
}
