Richard Redding
• Member for 5 years, 5 months
• Last seen more than a month ago
• Cambridge, United Kingdom

Mehrin, you are in danger of creating a false dichotomy. EM is an optimisation technique that can be used to find maximum likelihood estimates and so the choice is not "one or the other". In mixture ...

You need to transform your prediction back to the original space before calculating residuals rather than transforming the residuals from the log space. $\newcommand{\Exp}{\operatorname{Exp}} \... View answer Accepted answer 5 votes There is no such thing as a truly noninformative prior--even the prior you've selected for the mean is not uniform under a reparameterisation. Having said that, you could use proper uniform priors on ... View answer Accepted answer 3 votes Try simulating from a multivariate normal distribution and then transforming the values by using the normal cdf. This will produce correlated standard uniform variates. You can then shift and scale ... View answer 3 votes MCMC is typically used as an alternative to crude Monte Carlo simulation techniques. Both MCMC and other Monte Carlo techniques are used to evaluate difficult integrals but MCMC can be used more ... View answer 2 votes Ward's ESS is the same as the SS you mention. If you distribute the terms in your formula you get:$ \sum(x_i - \bar x)^2 = \sum x_i^2 + \sum \bar x^2 - 2 \bar x \sum x_i = \sum x_i^2 - n \bar x ^2 =...

So you can simulate pseudo observations given the parameters. In this case it might be helpful to use Approximate Bayesian Computation (ABC). In its crudest form you: define a distance metric ...

Making up a story would be dangerous but, to your general point, it depends. It obviously complicates any statistical methods you use but in many cases it would be wasteful to not contribute new ...

There's no right answer to this and you will have to be careful about how you synthesise these opinions with any hard data that you subsequently record. With those caveats out there, I'd recommend ...

... is it realistic to expect that one can actually find parametric forms for $\mu(x)$, $\sigma^2 (x)$ in order to "validate" the use of a Gaussian Process to fit the data? Yes, if I take "validate" ...

I have also been investigating merging observations when performing Gaussian Process regression. In my problem I have only one covariate. I'm not sure I necessarily agree that the Nystrom ...

In a sample there must be at least one record with a value less than the mean of the sample (unless all records are equal). Hence the greatest proportion of records greater than the mean is 1-1/n.

Depending on the sample size you could consider a permutation test. First calculate the difference in the means of the two clusters. Then estimate the distribution of the differences in the means of ...

Logistic regression is a GLM. The linear ("L") part of GLM relates to the fact that the covariates are included within a linear predictor. However, the linear predictor is transformed by a link ...

The tweedie distribution is in the exponential family, hence can be used in a GLM context, and it allows for the occurrence of zeroes in the response variable. You could see if this suits your purpose....

A bit late but an alternative to David's suggestion would be to combine your daily data sets. Assuming the mean function is constant from one set of measurements to the next you could create a ...

I think you are asking why the out of sample likelihood isn't used as the loss function for assessing accuracy. As a matter of fact it is, e.g. See http://ageconsearch.umn.edu/bitstream/18947/1/...