arXiv subjects

Nabil Kahale

Publications and source records attributed to Nabil Kahale.

8 recordsLinked to original sources

arXiv2023

Simulating Gaussian vectors via randomized dimension reduction and PCA

We study the problem of estimating E(g(X)), where g is a real-valued function of d variables and X is a d-dimensional Gaussian vector with a given covariance matrix. We present a new unbiased estimator for E(g(X)) that combines the randomized dimension reduction technique with principal components analysis. Under suitable conditions, we prove that our algorithm outperforms the standard Monte Carlo method by a factor of order d.

math.ST↗

arXiv2022

Unbiased time-average estimators for Markov chains

We consider a time-average estimator $f_{k}$ of a functional of a Markov chain. Under a coupling assumption, we show that the expectation of $f_{k}$ has a limit $\mu$ as the number of time-steps goes to infinity. We describe a modification of $f_{k}$ that yields an unbiased estimator $\hat f_{k}$ of $\mu$. It is shown that $\hat f_{k}$ is square-integrable and has finite expected running time. Under certain conditions, $\hat f_{k}$ can be built without any precomputations, and is asymptotically at least as efficient as $f_{k}$, up to a multiplicative constant arbitrarily close to $1$. Our approach provides an unbiased estimator for the bias of $f_{k}$. We study applications to volatility forecasting, queues, and the simulation of high-dimensional Gaussian vectors. Our numerical experiments are consistent with our theoretical findings.

math.ST↗

arXiv2021

Improved SVRG for quadratic functions

We analyse an iterative algorithm to minimize quadratic functions whose Hessian matrix $H$ is the expectation of a random symmetric $d\times d$ matrix. The algorithm is a variant of the stochastic variance reduced gradient (SVRG). In several applications, including least-squares regressions, ridge regressions, linear discriminant analysis and regularized linear discriminant analysis, the running time of each iteration is proportional to $d$. Under smoothness and convexity conditions, the algorithm has linear convergence. When applied to quadratic functions, our analysis improves the state-of-the-art performance of SVRG up to a logarithmic factor. Furthermore, for well-conditioned quadratic problems, our analysis improves the state-of-the-art running times of accelerated SVRG, and is better than the known matching lower bound, by a logarithmic factor. Our theoretical results are backed with numerical experiments.

cs.LG↗

arXiv2019

Optimal unbiased estimators via convex hulls

Necessary and sufficient conditions for the square-integrability of recently proposed unbiased estimators are established. A geometric characterization of a distribution that optimizes the performance of these estimators is given. An algorithm based on convex hulls that finds the optimal distribution truncated to its first m terms in time linear in m is described. The algorithm exploits a connection with a recent randomized dimension reduction method and is illustrated via a numerical example.

math.ST↗

arXiv2018

Randomized Dimension Reduction for Monte Carlo Simulations

We present a new unbiased algorithm that estimates the expected value of f(U) via Monte Carlo simulation, where U is a vector of d independent random variables, and f is a function of d variables. We assume that f does not depend equally on all its arguments. Under certain conditions we prove that, for the same computational cost, the variance of our estimator is lower than the variance of the standard Monte Carlo estimator by a factor of order d. Our method can be used to obtain a low-variance unbiased estimator for the expectation of a function of the state of a Markov chain at a given time-step. We study applications to volatility forecasting and time-varying queues. Numerical experiments show that our algorithm dramatically improves upon the standard Monte Carlo method for large values of d, and is highly resilient to discontinuities.

stat.CO↗

arXiv2018

General multilevel Monte Carlo methods for pricing discretely monitored Asian options

We describe general multilevel Monte Carlo methods that estimate the price of an Asian option monitored at $m$ fixed dates. Our approach yields unbiased estimators with standard deviation $O(\epsilon)$ in $O(m + (1/\epsilon)^{2})$ expected time for a variety of processes including the Black-Scholes model, Merton's jump-diffusion model, the Square-Root diffusion model, Kou's double exponential jump-diffusion model, the variance gamma and NIG exponential Levy processes and, via the Milstein scheme, processes driven by scalar stochastic differential equations. Using the Euler scheme, our approach estimates the Asian option price with root mean square error $O(\epsilon)$ in $O(m+(\ln(\epsilon)/\epsilon)^{2})$ expected time for processes driven by multidimensional stochastic differential equations. Numerical experiments confirm that our approach outperforms the conventional Monte Carlo method by a factor of order $m$.

q-fin.CP↗

arXiv2017

Efficient simulation of high dimensional Gaussian vectors

We describe a Markov chain Monte Carlo method to approximately simulate a centered d-dimensional Gaussian vector X with given covariance matrix. The standard Monte Carlo method is based on the Cholesky decomposition, which takes cubic time and has quadratic storage cost in d. In contrast, the storage cost of our algorithm is linear in d. We give a bound on the quadractic Wasserstein distance between the distribution of our sample and the target distribution. Our method can be used to estimate the expectation of h(X), where h is a real-valued function of d variables. Under certain conditions, we show that the mean square error of our method is inversely proportional to its running time. We also prove that, under suitable conditions, our method is faster than the standard Monte Carlo method by a factor nearly proportional to d. A numerical example is given.

stat.CO↗

arXiv2008

Analytic crossing probabilities for certain barriers by Brownian motion

We calculate crossing probabilities and one-sided last exit time densities for a class of moving barriers on an interval $[0,T]$ via Schwartz distributions. We derive crossing probabilities and first hitting time densities for another class of barriers on $[0,T]$ by proving a Schwartz distribution version of the method of images. Analytic expressions for crossing probabilities and related densities are given for new explicit and semi-explicit barriers.

math.PR↗