One of the most important aspects in asset allocation problems is the assumption concerning the probability distribution of asset returns. Financial managers generally suppose normal distribution, even if extreme realizations usually have an higher frequency than in the Gaussian case. We propose a general Monte Carlo simulation approach in order to solve an asset allocation problem with shortfa...

Traditionally, the process capability index is developed assuming that the process output data are independent and follow normal distribution. However, in most environmental cases, the process data have more than one quality characteristic and exhibit property of autocorrelation. We propose two novel multivariate process capability indices for autocorrelated data, NMACp and NMACpm for the-nomin...

mining companies to determine a resource model of the tonnage and head grade of a potential orebody, which is one of the first and most critical inputs underpinning any mining project. The prediction of the grade, tonnage, and recoverable metal for a particular mining plan and the corresponding financial forecast constitute the main technical risks in mineral resource evaluation. The convention...

The paper introduces a new non-Gaussian simulation method that matches a target power spectrum and probability information. Numerical studies demonstrate applicability, convergence, and stationary properties. The method is shown to encompass a larger envelope of spectral/probabilistic descriptions than correlation distortion based methods. Specifically, the method is not constrained to relative...

We develop an observation that a simulation method introduced recently for heavy-tailed stochastic simulation, namely hazard-rate twisting, is equivalent to doing exponential twisting on a transformed version of the heavy-tailed random-variable; the transforming function is the hazard function. Using this approach, the paper develops efficient methods for computing portfolio value-at-risk (VAR)...

rock selection in modeling and simulation studies is usually based on two techniques; routinely defined rock types and those defined by special core analysis (scal). the challenge in utilizing these two techniques is that they are frequently assumed to be the same, but in practice, static rock-types (routinely defined) are not always representative of dynamic rock-types (scal defined) in the re...

BACKGROUND Multiple imputation is a popular approach to handling missing data in medical research, yet little is known about its applicability for estimating the relative risk. Standard methods for imputing incomplete binary outcomes involve logistic regression or an assumption of multivariate normality, whereas relative risks are typically estimated using log binomial models. It is unclear whe...

Multivariate generalized hyperbolic distributions represent an attractive family of distributions (with exponentially decreasing tails) for multivariate data modelling. However, in a limited data environment, robust and fast estimation procedures are rare. In this paper we propose an alternative class of multivariate distributions (with exponentially decreasing tails) belonging to affine-linear...

In application areas like bioinformatics multivariate distributions on angles are encountered which show significant clustering. One approach to statistical modelling of such situations is to use mixtures of unimodal distributions. In the literature (Mardia et al., 2011), the multivariate von Mises distribution, also known as the multivariate sine distribution, has been suggested for components...

Multivariate generalized hyperbolic distributions represent an attractive family of distributions (with exponentially decreasing tails) for multivariate data modelling. However, in a limited data environment, robust and fast estimation procedures are rare. In this paper we propose an alternative class of multivariate distributions (with exponentially decreasing tails) belonging to affine-linear...