In this post, we will build the samplers for the item parameters using the generic functions developed in Post 5 and Post 6. We will check that the samplers work by running them on the fake data from Post 2, … Continue reading →

Let us continue our discussion on smoothing techniques in regression. Assume that . where is some unkown function, but assumed to be sufficently smooth. For instance, assume that is continuous, that exists, and is continuous, that exists and is also continuous, etc. If is smooth enough, Taylor’s expansion can be used. Hence, for which can also be writen as for...

In a standard linear model, we assume that . Alternatives can be considered, when the linear assumption is too strong. Polynomial regression A natural extension might be to assume some polynomial function, Again, in the standard linear model approach (with a conditional normal distribution using the GLM terminology), parameters can be obtained using least squares, where a regression of...

My office computer recently got a really nice upgrade and now I have 8 cores on my desktop to play with. I also at the same time received some code for a Gibbs sampler written in R from my adviser. I wanted to try a metropolis-coupled markov chain monte carlo, , algorithm on it to The post Parallel...

There are many kinds of intervals in statistics. To name a few of the common intervals: confidence intervals, prediction intervals, credible intervals, and tolerance intervals. Each are useful and serve their own purpose. I’ve been recently working on a couple of projects that involve making predictions from a regression model and I’ve been doing some

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