       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.

```

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