Probit in a Spatial Context: A Monte Carlo Analysis

Advances in Spatial Econometrics - Advances in Spatial Science ◽

10.1007/978-3-662-05617-2_8 ◽

2004 ◽

pp. 169-195 ◽

Cited By ~ 52

Author(s):

Kurt J. Beron ◽

Wim P. M. Vijverberg

Keyword(s):

Monte Carlo ◽

Monte Carlo Analysis ◽

Spatial Context

Download Full-text

Facing Challenges for Monte Carlo Analysis of Full PWR Cores : Towards Optimal Detail Level for Coupled Neutronics and Proper Diffusion Data for Nodal Kinetics

SNA + MC 2013 - Joint International Conference on Supercomputing in Nuclear Applications + Monte Carlo ◽

10.1051/snamc/201402202 ◽

2014 ◽

Author(s):

A. Nuttin ◽

N. Capellan ◽

S. David ◽

X. Doligez ◽

C. El Mhari ◽

...

Keyword(s):

Monte Carlo ◽

Monte Carlo Analysis ◽

Diffusion Data

Download Full-text

Generation of Correlated Logistic-Normal Random Variates for Medical Decision Trees

Methods of Information in Medicine ◽

10.1055/s-0038-1634534 ◽

1998 ◽

Vol 37 (03) ◽

pp. 235-238 ◽

Cited By ~ 1

Author(s):

M. El-Taha ◽

D. E. Clark

Keyword(s):

Monte Carlo ◽

Decision Trees ◽

Computer Algebra ◽

Taylor Series ◽

Computer Algebra System ◽

Random Variable ◽

Monte Carlo Analysis ◽

Normal Random Variable ◽

Medical Decision ◽

Theoretical Results

AbstractA Logistic-Normal random variable (Y) is obtained from a Normal random variable (X) by the relation Y = (ex)/(1 + ex). In Monte-Carlo analysis of decision trees, Logistic-Normal random variates may be used to model the branching probabilities. In some cases, the probabilities to be modeled may not be independent, and a method for generating correlated Logistic-Normal random variates would be useful. A technique for generating correlated Normal random variates has been previously described. Using Taylor Series approximations and the algebraic definitions of variance and covariance, we describe methods for estimating the means, variances, and covariances of Normal random variates which, after translation using the above formula, will result in Logistic-Normal random variates having approximately the desired means, variances, and covariances. Multiple simulations of the method using the Mathematica computer algebra system show satisfactory agreement with the theoretical results.

Download Full-text