albinali wrote: > Hi, > I am using a set of data that I collected using biosensors, the goal is to > analyze the data
Okay, let's stop right there. We all try to analyze data. What specifically does "analyze the data" mean in your application? > and to infer the dependencies between the variables to More problem definition would be good here, not in statistical terms, but in terms of your application. And what "variables" do you mean? If you mean predictor variables (the biosensors, I assume), that's one thing; if you mean between the biosensors and the mood (mentioned below) then that's a different thing altogether. > construct a bayesian network that infers the mood of a person (e.g. happy, > sad...etc). Oh, why didn't you say so? This is a very easy problem. You can determine the person's mood by the little smiley face in his e-mail. :-) Okay, I'm being facetious here, back to serious now. > Clearly, the response variable is non-ordinal and categorical. I'm not sure about non-ordinal, but it certainly seems to be categorical. Is it univariate or multivariate? > The model should not include interdependent sensors. There are only two ways to get non-interdependent sensors, if I am understanding you properly. One way is to design an orthogonal experiment, and since you are talking about biosensors, I don't think you can do that here. The second way is to take the sensors you have and find orthogonal combinations of them (in effect, computing new sensors) by something like Principal Components Analysis (PCA) or Partial Least Squares (PLS). These two methods are also useful if I understand your earlier stated need "to infer the dependencies between the variables". > I would like to get > some thoughts about using regression for such a task, is it a good choice ? "Regression" is a broad term. In one sense of the word, regression can include any predictive method, such as logistic regression, neural networks and the aforementioned Bayesian networks. Any of those might be good candidates for your case, which does not have a continuous response variable. Ordinary Least Squares, which is a common use of the term "regression", does not seem to fit because your response is not continuous. > are there any other possible techniques to use? I think I have mentioned a few. > More importantly, would it > be possible to automate the building of such models? Yes, but this can be very dangerous. > Is anyone aware of any earlier work in that area? I'd bet the answer is YES to this question too. -- Paige Miller Eastman Kodak Company [EMAIL PROTECTED] http://www.kodak.com "It's nothing until I call it!" -- Bill Klem, NL Umpire "When you get the choice to sit it out or dance, I hope you dance" -- Lee Ann Womack . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
