# mle.tmvnorm: Maximum Likelihood Estimation for the Truncated Multivariate... In tmvtnorm: Truncated Multivariate Normal and Student t Distribution

## Description

Maximum Likelihood Estimation for the Truncated Multivariate Normal Distribution

## Usage

 1 2 3 4 5 6 7 8 9 mle.tmvnorm(X, lower = rep(-Inf, length = ncol(X)), upper = rep(+Inf, length = ncol(X)), start = list(mu = rep(0, ncol(X)), sigma = diag(ncol(X))), fixed = list(), method = "BFGS", cholesky = FALSE, lower.bounds = -Inf, upper.bounds = +Inf, ...) 

## Arguments

 X Matrix of quantiles, each row is taken to be a quantile. lower Vector of lower truncation points, default is rep(-Inf, length = ncol(X)). upper Vector of upper truncation points, default is rep( Inf, length = ncol(X)). start Named list with elements mu (mean vector) and sigma (covariance matrix). Initial values for optimizer. fixed Named list. Parameter values to keep fixed during optimization. method Optimization method to use. See optim cholesky if TRUE, we use the Cholesky decomposition of sigma as parametrization lower.bounds lower bounds/box constraints for method "L-BFGS-B" upper.bounds upper bounds/box constraints for method "L-BFGS-B" ... Further arguments to pass to optim

## Details

This method performs a maximum likelihood estimation of the parameters mean and sigma of a truncated multinormal distribution, when the truncation points lower and upper are known. mle.tmvnorm() is a wrapper for the general maximum likelihood method mle, so one does not have to specify the negative log-likelihood function.

The log-likelihood function for a data matrix X (T x n) can be established straightforward as

\log L(X | μ,Σ) = -T \log{α(μ,Σ)} + {-T/2} \log{\|Σ\|} -\frac{1}{2} ∑_{t=1}^{T}{(x_t-μ)' Σ^{-1} (x_t-μ)}

As mle, this method returns an object of class mle, for which various diagnostic methods are available, like profile(), confint() etc. See examples.

In order to adapt the estimation problem to mle, the named parameters for mean vector elements are "mu_i" and the elements of the covariance matrix are "sigma_ij" for the lower triangular matrix elements, i.e. (j <= i).

## Value

An object of class mle-class

## Author(s)

Stefan Wilhelm [email protected]

mle and mle-class
  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 ## Not run: set.seed(1.2345) # the actual parameters lower <- c(-1,-1) upper <- c(1, 2) mu <- c(0, 0) sigma <- matrix(c(1, 0.7, 0.7, 2), 2, 2) # generate random samples X <- rtmvnorm(n=500, mu, sigma, lower, upper) method <- "BFGS" # estimate mean vector and covariance matrix sigma from random samples X # with default start values mle.fit1 <- mle.tmvnorm(X, lower=lower, upper=upper) # diagnostic output of the estimated parameters summary(mle.fit1) logLik(mle.fit1) vcov(mle.fit1) # profiling the log likelihood and confidence intervals mle.profile1 <- profile(mle.fit1, X, method="BFGS", trace=TRUE) confint(mle.profile1) par(mfrow=c(3,2)) plot(mle.profile1) # choosing a different start value mle.fit2 <- mle.tmvnorm(X, lower=lower, upper=upper, start=list(mu=c(0.1, 0.1), sigma=matrix(c(1, 0.4, 0.4, 1.8),2,2))) summary(mle.fit2) ## End(Not run)