MONTE CARLO EVALUATION OF ERROR RATE ESTIMATORS IN DISCRIMINANT ANALYSIS UNDER MULTIVARIATE NORMAL DATA

Multivariate initial sequence estimators in Markov chain Monte Carlo

Markov chain Monte Carlo (MCMC) is a simulation method commonly used for estimating expectations with respect to a given distribution. We consider estimating the covariance matrix of the asymptotic multivariate normal distribution of a vector of sample means. Geyer [9] developed a Monte Carlo error estimation method for estimating a univariate mean. We propose a novel multivariate version of Ge...

Monte Carlo Analysis of Change Point Estimators

We consider several estimators for the change point in a sequence of independent observations. These are defined as the maximizing points of usually used statistics for nonparametric change point detection problems. Our investigations focus on the non asymptotic behaviour of the proposed estimators for sample sizes commonly observed in practice. We conducted a broad Monte Carlo study to compare...

Error in Monte Carlo, quasi-error in Quasi-Monte Carlo

While the Quasi-Monte Carlo method of numerical integration achieves smaller integration error than standard Monte Carlo, its use in particle physics phenomenology has been hindered by the abscence of a reliable way to estimate that error. The standard Monte Carlo error estimator relies on the assumption that the points are generated independently of each other and, therefore, fails to account ...

Monte Carlo error estimation for multivariate Markov chains

In this paper, the conservative Monte Carlo error estimation methods and theory developed in Geyer (1992a) are extended from univariate to multivariate Markov chain applications. A small simulation study demonstrates the feasibility of the proposed estimators.

Analysis of Estimators for Adaptive Kinetic Monte Carlo