Using Monte Carlo simulations we analyze the potential of the upcoming transit survey Pan-Planets. The analysis covers the simulation of realistic light curves (including the effects of ingress/egress and limb-darkening) with both correlated and uncorrelated noise as well as the application of a box-fitting-least-squares detection algorithm. In this work we show how simulations can be a powerfu...

Recently, various authors proposed Monte-Carlo methods for the computation of American option prices, based on least squares regression. The purpose of this paper is to analyze an algorithm due to Longstaff and Schwartz. This algorithm involves two types of approximation. Approximation one: replace the conditional expectations in the dynamic programming principle by projections on a finite set ...

A recently introduced Importance Sampling strategy based on a least squares optimization is applied to the Monte Carlo simulation of Libor Market Models. Such Least Squares Importance Sampling (LSIS) allows the automatic optimization of the sampling distribution within a trial class by means of a quick presimulation algorithm of straightforward implementation. With several numerical examples we...

In this paper, we propose an -stable 2D extension of the 1D fractional Gaussian noise. The considered model exhibits long-range dependence properties, while its stability index allows us to control the degree of spikyness of the synthesized fields. A least square method for the estimation of the three parameters of this model is also described and its performances are evaluated by a Monte Carlo...

The authors present and analyse a predictive closed-loop power control (CLPC) scheme, which employs a comb-type sample arrangement to effectively compensate multiple power control group delays over mobile fading channels. The authors consider both least squares and recursive least squares filters in our CLPC scheme. The effects of channel estimation error, prediction filter error and power cont...

We propose extensions and improvements of the statistical analysis of distributed multipoles (SADM) algorithm put forth by Chipot et al in [6] for the derivation of distributed atomic multipoles from the quantum-mechanical electrostatic potential. The method is mathematically extended to general least-squares problems and provides an alternative approximation method in cases where the original ...

The composite-factor estimation dichotomy has been the epicenter of a long and ongoing debate among proponents and detractors of the use of the partial least squares (PLS) approach for structural equation modeling (SEM). In this brief research note we discuss the implementation of a new method to conduct factor-based PLS-SEM analyses, which could be a solid step in the resolution of this debate...

We propose a method for pricing American options whose pay-off depends on the moving average of the underlying asset price. The method uses a finite dimensional approximation of the infinite-dimensional dynamics of the moving average process based on a truncated Laguerre series expansion. The resulting problem is a finite-dimensional optimal stopping problem, which we propose to solve with a le...

In a recent paper Longstaff & Schwartz (2001) suggest a method to American option valuation based on simulation. The method is termed the Least Squares Monte-Carlo (LSM) method, and although it has become widely used not much is known about the properties of the estimator. This paper corrects this shortcoming using theory from the literature on seminonparametric series estimators. A central par...

Traditional regression assumes that the only data available are measurements of the value of the dependent variable for each combination of values for the independent variable. However, in many settings found in stochastic (Monte Carlo) simulation, directly estimated derivative information is also available via techniques such as perturbation analysis or the likelihood ratio method. In this pap...