✦ LIBER ✦

Numerical approximation of conditional asymptotic variances using Monte Carlo simulation

✍ Scribed by Tak K. Mak; Fassil Nebebe

Publisher: Springer
Year: 2008
Tongue: English
Weight: 154 KB
Volume: 24
Category: Article
ISSN: 0943-4062
DOI: 10.1007/s00180-008-0121-0

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

The use of Monte Carlo simulations to ev

The use of Monte Carlo simulations to evaluate kinetic data and analytic approximations

✍ Jan P. Hessler 📂 Article 📅 1997 🏛 John Wiley and Sons 🌐 English ⚖ 285 KB 👁 2 views

Experimental kineticists are always faced with the problem of reducing kinetic data to extract physically meaningful information. A particularly vexing problem arises when different models reproduce the data but yield different values for the physical parameters. For over forty-five years Monte Carl

New estimates of the variance in the Mon

New estimates of the variance in the Monte Carlo method when an approximate value function is used

✍ A.V. Starkov 📂 Article 📅 1986 🏛 Elsevier Science ⚖ 496 KB

Monte Carlo probabilistic sensitivity an

Monte Carlo probabilistic sensitivity analysis for patient level simulation models: efficient estimation of mean and variance using ANOVA

✍ Anthony O'Hagan; Matt Stevenson; Jason Madan 📂 Article 📅 2007 🏛 John Wiley and Sons 🌐 English ⚖ 185 KB

## Abstract Probabilistic sensitivity analysis (PSA) is required to account for uncertainty in cost‐effectiveness calculations arising from health economic models. The simplest way to perform PSA in practice is by Monte Carlo methods, which involves running the model many times using randomly sampl

Ant colony method to control variance re

Ant colony method to control variance reduction techniques in the Monte Carlo simulation of clinical electron linear accelerators of use in cancer therapy

✍ S. García-Pareja; M. Vilches; A.M. Lallena 📂 Article 📅 2010 🏛 Elsevier Science 🌐 English ⚖ 570 KB

The Monte Carlo simulation of clinical electron linear accelerators requires large computation times to achieve the level of uncertainty required for radiotherapy. In this context, variance reduction techniques play a fundamental role in the reduction of this computational time. Here we describe the