# Tag Info

## Hot answers tagged multivariate-regression

### Explain the difference between multiple regression and multivariate regression, with minimal use of symbols/math

Simple regression pertains to one dependent variable ($y$) and one independent variable ($x$): $y = f(x)$ Multiple regression (aka multivariable regression) pertains to one dependent variable and ...
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### Why do we need multivariate regression (as opposed to a bunch of univariate regressions)?

Be sure to read the full example on the UCLA site that you linked. Regarding 1: Using a multivariate model helps you (formally, inferentially) compare coefficients across outcomes. In that linked ...
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### Multivariate linear regression with lasso in r

For multivariate responses (number of dependent variables larger than 1), you need family = "mgaussian" in the call of glmnet. ...
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### Why do we need multivariate regression (as opposed to a bunch of univariate regressions)?

Think about all the false and sometimes dangerous conclusions that come from simply multiplying probabilities, thinking events are independent. Because of all the built-in redundant safeguards, we ...
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### gaussian process regression for large datasets

You asked: in the case where š is 10's of millions does Gaussian process regression still work? Not in the standard sense of constructing and inverting a large matrix. You have two options: 1)...

### Explain the difference between multiple regression and multivariate regression, with minimal use of symbols/math

I think the key insight (and differentiator) here aside from the number of variables on either side of the equation is that for the case of multivariate regression, the goal is to utilize the fact ...
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### Neural network for multiple output regression

At first I thought generic_user's comment was a show-stopper, but I just realized it isn't: If I train d different networks on d different outputs, then each one will be fit to that dimension with no ...
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### gaussian process regression for large datasets

There are a wide range of approaches to scale GPs to large datasets, for example: Low Rank Approaches: these endeavoring to create a low rank approximation to the covariance matrix. Most famously ...
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