This paper has several folds. We make first new permutation choices in Halton sequences to improve their distributions. These choices are multi-dimensional and they are made for two different discrepancies. We show that multi-dimensional choices are better for standard quasi-Monte Carlo methods. We also use these sequences as a variance reduction technique in Monte Carlo methods, which greatly ...

In this paper, control variates are proposed to speed up Monte Carlo Simulations to estimate expected error rates in multivariate classiication.

Consider a Gaussian nonparametric regression problem having both an unknown mean function and unknown variance function. This article presents a class of difference-based kernel estimators for the variance function. Optimal convergence rates that are uniform over broad functional classes and bandwidths are fully characterized, and asymptotic normality is also established. We also show that for ...

The present study investigates the relationship of self-report inventories of ”sense of humor” and behavioral measures of humor as well as their location in the Eysenckian PEN system. 110 male and female adults ages 17 to 83 answered the following inventories: SHRQ (Martin and Lefcourt 1984), SHQZ (Ziv 1981), SHQ-3 revised (Svebak 1993), CHS (Martin and Lefcourt 1983), MSHS (Thorson and Powell ...

one of the issues that in recent decades in the field of information economy is rapidly expanding economy under information and discussion in this major economic problem of asymmetric information is related. as we know in today's world information and trading stock market performance is the core. lateral velocity and symmetry of the main role of information and market efficiency is critical. on...

This paper presents an overview of techniques for improving the efficiency of option pricing simulations, including quasi-Monte Carlo methods, variance reduction, and methods for dealing with discretization error.

The feature hashing algorithm introduced by Weinberger et al. (2009) is a popular dimensionality reduction that compresses high dimensional data points into low closely approximate the pairwise inner product. This has been used in many fundamental machine learning applications such as model compression (Chen 2015), spam classification (Weinberger 2009), compressing text classifiers (Joulin 2016...