Sigman 1 Estimating sensitivities

Sigman 1 Geometric Brownian motion

Sigman 1 IEOR 4700 : Introduction to stochastic integration

(h(tj−1) can be replaced by h(sj), for any sj ∈ [tj−1, tj ].) As n gets larger and larger while the partition gets finer and finer, the approximating sum to the true area under the function becomes exact. This “dt” integration can be generalized to increments “dG(t)” of (say) any monotone increasing function G(t) by using G(tj) − G(tj−1) in place of tj − tj−1 yielding the so-called Riemann-Stie...

RAPPORT A note on negative customers , GI / G / 1 workload , and risk processes

Recently the workload distribution in the M/G/1 queue with work removal has been analysed , and has been shown to exhibit a generalized Pollaczek-Khintchine form. The latter result is explained in this note by transforming the model into a standard GI/G/1 queue. Some extensions are also discussed, as well as a connection with a `dual' risk process.

Conditional Monte Carlo Estimation of Quantile Sensitivities

E quantile sensitivities is important in many optimization applications, from hedging in financial engineering to service-level constraints in inventory control to more general chance constraints in stochastic programming. Recently, Hong (Hong, L. J. 2009. Estimating quantile sensitivities. Oper. Res. 57 118–130) derived a batched infinitesimal perturbation analysis estimator for quantile sensi...

Estimation of an individual's Human Cone Fundamentals from their Color Matching Functions

Estimating individual cone sensitivities on the basis of their corresponding individual color matching functions is a classic problem of color vision. To solve the problem one has, in effect, to postulate constraints on the shape of the cone sensitivities. For example, Logvinenko applied a linear matching method (originally proposed by Bongard and Smirnov [4]) which excludes all but one primary...