Statistics

Statistics Blogs

The case for index-free data manipulation

December 10, 2016
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The case for index-free data manipulation

Statisticians and data scientists want a neat world where data is arranged in a table such that every row is an observation or instance, and every column is a variable or measurement. Getting to this state of “ready to model format” (often called a denormalized form by relational algebra types) often requires quite a bit … Continue reading The case for index-free data manipulation

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Nomen omen

December 9, 2016
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Nomen omen

After resisting this for way too long, I've finally decided it was time to release more widely a couple of the R packages I've been working on $-$ I've put them on GitHub, hence the mug...In both cases, while I think the packages do work nicely, I am s...

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Which graph to use?

December 8, 2016
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Which graph to use?

A student asked me on our Facebook page to help with an assignment. It got me thinking again about the nature of answers in statistics, and the challenge of communicating through graphs. The student gave no explanation, but rather a … Continue reading →

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Using replyr::let to Parameterize dplyr Expressions

December 7, 2016
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Using replyr::let to Parameterize dplyr Expressions

Imagine that in the course of your analysis, you regularly require summaries of numerical values. For some applications you want the mean of that quantity, plus/minus a standard deviation; for other applications you want the median, and perhaps an interval around the median based on the interquartile range (IQR). In either case, you may want … Continue reading Using replyr::let to Parameterize dplyr Expressions

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10 hints to make the most of teaching and academic conferences

December 6, 2016
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10 hints to make the most of teaching and academic conferences

Hints for conference benefit maximisation I am writing this post in a spartan bedroom in Glenn Hall at La Trobe University in Bundoora (Melbourne, Australia.) Some outrageously loud crows are doing what crows do best outside my window, and I … Continue reading →

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Monte Carlo Simulation Basics, III: Regression Model Estimators

December 5, 2016
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Monte Carlo Simulation Basics, III: Regression Model Estimators

This post is the third in a series of posts that I'm writing about Monte Carlo (MC) simulation, especially as it applies to econometrics. If you've already seen the first two posts in the series (here and here) then you'll know that my intention is to ...

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Monte Carlo Simulation Basics, III: Regression Model Estimators

December 5, 2016
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Monte Carlo Simulation Basics, III: Regression Model Estimators

This post is the third in a series of posts that I'm writing about Monte Carlo (MC) simulation, especially as it applies to econometrics. If you've already seen the first two posts in the series (here and here) then you'll know that my intention is to ...

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Exogenous vs. Endogenous Volatility Dynamics

December 5, 2016
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I always thought putting exogenous volatility dynamics in macro-model shocks was a cop-out.  Somehow it seemed more satisfying for volatility to be determined endogenously, in equilibrium.  Then I came around:  We allow for shocks with e...

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December Reading List

December 3, 2016
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December Reading List

Goodness me! November went by really quickly!Bagnato, L., L. De Capitani, & A. Punzo, 2016. Testing for serial independence: Beyond the portmanteau approach. American Statistician, in press.Aastveit, K.A., C. Foroni, & F. Ravazzolo, 2016. Densi...

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Be careful evaluating model predictions

December 3, 2016
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Be careful evaluating model predictions

One thing I teach is: when evaluating the performance of regression models you should not use correlation as your score. This is because correlation tells you if a re-scaling of your result is useful, but you want to know if the result in your hand is in fact useful. For example: the Mars Climate Orbiter … Continue reading Be careful evaluating model predictions

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