hess_l_mvn_generic: Hessian Matrix of the Multivariate Normal Distribution -...

In jeksterslab/gammaMatrix: Asymptotic Covariance Matrix of Covariances

Description

Calculates hessian matrix of the log of the likelihood function of the multivariate normal distribution for the ith observation.

Usage

hess_l_mvn_generic(x, mu, sigmacap)

Arguments

x	Numeric vector of length k. The ith vector of observations.
mu	Numeric vector. Parameter. Mean vector \boldsymbol{μ}.
sigmacap	Numeric matrix. Parameter. Covariance matrix \boldsymbol{Σ}.

Value

A matrix.

See Also

Other Multivariate Normal Distribution Functions: grad_l_mvn_generic(), grad_l_mvn(), hess_l_mvn(), l_mvn_generic(), l_mvn(), mvn_theta_helper(), negl_mvn(), rmvn_chol()

Examples

n <- 5
mu <- c(0, 0)
sigmacap <- matrix(
  data = c(
    1, 0.5, 0.5, 1
  ),
  nrow = 2
)

xcap <- as.data.frame(
  t(
    rmvn_chol(
      n = n,
      mu = mu,
      sigmacap = sigmacap
    )
  )
)

lapply(
  X = xcap,
  FUN = hess_l_mvn_generic,
  mu = mu,
  sigmacap = sigmacap
)

jeksterslab/gammaMatrix documentation built on Dec. 20, 2021, 10:10 p.m.