Re: Large control loops

*To*: mathgroup at smc.vnet.net*Subject*: [mg122331] Re: Large control loops*From*: Arthur <shukrri at gmail.com>*Date*: Tue, 25 Oct 2011 06:18:38 -0400 (EDT)*Delivered-to*: l-mathgroup@mail-archive0.wolfram.com*References*: <j80qff$ael$1@smc.vnet.net> <j83aon$k7c$1@smc.vnet.net>

Hi, The vector in the first column is the common vector. It is the independent variable in the subsequent regressions - I am regressing first to find the correlation between it and each of the other vectors. It's all numbers. OLS regressions. I am estimating the parameters through a secondary regression on the residuals of the first and then some basic algebra. It's an Ornstein- Uhlenbeck process, I am using the first procedure outlined here http://www.sitmo.com/article/calibrating-the-ornstein-uhlenbeck-model/ I am not so much worried about the time (although that too is a concern) but the actual structure. Should it just be a long while loop? Thanks, A. > You haven't said what the elements of A and the common vector are. > Are they all numeric? If so then make sure they're packed arrays. > > Also, you haven't said which direction the regressions are going. > Is the common vector the predictor or the response? > > What kind of regressions? Ordinary least-squares linear, with an > intercept? Or something more complicated? > > And how are you estimating the stochastic process parameters? > Is this step likely to take most of the time? > > All I can suggest now is that things will probably go faster if > you transpose A so that you work with rows instead of columns.