Blog Archives

The “probability to win” is hard to estimate…

November 6, 2018
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The “probability to win” is hard to estimate…

Real-time computation (or estimation) of the “probability to win” is difficult. We’ve seem that in soccer games, in elections… but actually, as a professor, I see that frequently when I grade my students. Consider a classical multiple choice exam. After each question, imagine that you try to compute the probability that the student will pass. Consider here the case...

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Solving the chinese postman problem

October 19, 2018
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Solving the chinese postman problem

Some pre-Halloween post today. It started actually while I was in Barcelona : kids wanted to go back to some store we’ve seen the first day, in the gothic part, and I could not remember where it was. And I said to myself that would be quite long to do all the street of the neighborhood. And I discovered...

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Monte Carlo techniques to create counterfactuals

October 11, 2018
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Monte Carlo techniques to create counterfactuals

In the previous STT5100 course, last week, we’ve seen how to use monte carlo simulations. The idea is that we do observe in statistics a sample , and more generally, in econometrics . But let’s get back to statistics (without covariates) to illustrate. We assume that observations are realizations of an underlying random variable . We assume that are...

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October, grant proposal season

October 9, 2018
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October, grant proposal season

In 2012, Danielle Herbert, Adrian Barnett, Philip Clarke and Nicholas Graves published an article entitled “on the time spent preparing grant proposals: an observational study of Australian researchers“, whose conclusions had been included in Nature under a more explicit title, “Australia’s grant system wastes time” ! In this study, they included 3700 grant applications sent to the National Health...

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Combining automatically factor levels in R

October 6, 2018
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Combining automatically factor levels in R

Each time we face real applications in an applied econometrics course, we have to deal with categorial variables. And the same question arise, from students : how can we combine automatically factor levels ? Is there a simple R function ? I did upload a few blog posts, over the pas years. But so far, nothing satistfying. Let me...

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Convex Regression Model

July 5, 2018
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Convex Regression Model

This morning during the lecture on nonlinear regression, I mentioned (very) briefly the case of convex regression. Since I forgot to mention the codes in R, I will publish them here. Assume that where is some convex function. Then is convex if and only if , , Hidreth (1954) proved that ifthen is unique. Let , then where. I.e....

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Game of Friendship Paradox

June 27, 2018
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Game of Friendship Paradox

In the introduction of my course next week, I will (briefly) mention networks, and I wanted to provide some illustration of the Friendship Paradox. On network of thrones (discussed in Beveridge and Shan (2016)), there is a dataset with the network of characters in Game of Thrones. The word “friend” might be abusive here, but let’s continue to call...

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Parallelizing Linear Regression or Using Multiple Sources

June 21, 2018
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Parallelizing Linear Regression or Using Multiple Sources

My previous post was explaining how mathematically it was possible to parallelize computation to estimate the parameters of a linear regression. More speficially, we have a matrix which is matrix and a -dimensional vector, and we want to compute by spliting the job. Instead of using the observations, we’ve seen that it was to possible to compute “something” using...

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Linear Regression, with Map-Reduce

June 18, 2018
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Sometimes, with big data, matrices are too big to handle, and it is possible to use tricks to numerically still do the map. Map-Reduce is one of those. With several cores, it is possible to split the problem, to map on each machine, and then to agregate it back at the end. Consider the case of the linear regression,...

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Quantile Regression (home made)

June 14, 2018
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Quantile Regression (home made)

After my series of post on classification algorithms, it’s time to get back to R codes, this time for quantile regression. Yes, I still want to get a better understanding of optimization routines, in R. Before looking at the quantile regression, let us compute the median, or the quantile, from a sample. Median Consider a sample . To compute...

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