Re: Re: Simulating Correlated non-Normal Random Variables
- To: mathgroup at smc.vnet.net
- Subject: [mg32594] Re: [mg32476] Re: Simulating Correlated non-Normal Random Variables
- From: "Johannes Ludsteck" <johannes.ludsteck at wiwi.uni-regensburg.de>
- Date: Thu, 31 Jan 2002 01:45:16 -0500 (EST)
- Organization: Universitaet Regensburg
- Sender: owner-wri-mathgroup at wolfram.com
Erich Neuwirth wrote: >>you can apply the same method to any set of independent variables >> >>when x1 ... x1 are independent variables >>and A is a matrix >> >>A.(x1,..xn) >> >>has covariance matrix A'A >> >>no normality assumptions are needed for that. The statement concerning the covariance Matrix ist correct, but it is not clear in general, whether you obtain a multivariate beta-distribution if you apply this transformation to a vector of univariate beta-distributed random variables. The invariance property (linear combinations of normal deviates are again normal) is a special property of normal deviates. If this is not clear to you, please try adding two uniform deviates with different support. Best regards, Johannes <><><><><><><><><><><><><><><><><><> Johannes Ludsteck Institut fuer Volkswirtschaftslehre Lehrstuhl Prof. Dr. Moeller Universitaet Regensburg Universitaetsstrasse 31 93053 Regensburg Tel +49/0941/943-2741