Simultaneous confidence intervals for a multivariate mean

December 7, 2016
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Simultaneous confidence intervals for a multivariate mean

Many SAS procedure compute statistics and also compute confidence intervals for the associated parameters. For example, PROC MEANS can compute the estimate of a univariate mean, and you can use the CLM option to get a confidence interval for the population mean. Many parametric regression procedures (such as PROC GLM) […] The post Simultaneous confidence intervals for a multivariate mean appeared first on The DO Loop.

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The EagerEyes Holiday Shopping Guide

December 7, 2016
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The EagerEyes Holiday Shopping Guide

Are you looking for the perfect gift for the data or visualization geek in your life? Did that crazy self-driving water bottle Kickstarter still not deliver, leaving you hunting for an overpriced Nintendo Classic? The EagerEyes Holiday Shopping Guide has all the geeky, uncool gifts you could possibly want. To be clear, none of the […]

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Hey, I forgot to include a cat picture in my previous post!

December 7, 2016
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Hey, I forgot to include a cat picture in my previous post!

Josh Miller fixes it for me: The post Hey, I forgot to include a cat picture in my previous post! appeared first on Statistical Modeling, Causal Inference, and Social Science.

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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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the incredible accuracy of Stirling’s approximation

December 6, 2016
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the incredible accuracy of Stirling’s approximation

The last riddle from the Riddler [last before The Election] summed up to find the probability of a Binomial B(2N,½) draw ending up at the very middle, N. Which is If one uses the standard Stirling approximation to the factorial function, log(N!)≈Nlog(N) – N + ½log(2πN) the approximation to ℘ is 1/√πN, which is not […]

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Hot hand 1, WSJ 0

December 6, 2016
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Hot hand 1, WSJ 0

In a generally good book review on “uncertainty and the limits of human reason,” William Easterly writes: Failing to process uncertainty correctly, we attach too much importance to too small a number of observations. Basketball teams believe that players suddenly have a “hot hand” after they have made a string of baskets, so you should […] The post Hot hand 1, WSJ 0 appeared first on Statistical Modeling, Causal Inference,…

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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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Data 1, NPR 0

December 6, 2016
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Data 1, NPR 0

Jay “should replace the Brooks brothers on the NYT op-ed page” Livingston writes: There it was again, the panic about the narcissism of millennialas as evidenced by selfies. This time it was NPR’s podcast Hidden Brain. The show’s host Shankar Vedantam chose to speak with only one researcher on the topic – psychologist Jean Twenge, […] The post Data 1, NPR 0 appeared first on Statistical Modeling, Causal Inference, and…

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best algorithm EVER !!!!!!!!

December 6, 2016
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best algorithm EVER !!!!!!!!

Someone writes: On the website https://odajournal.com/ you find a lot of material for Optimal (or “optimizing”) Data Analysis (ODA) which is described as: In the Optimal (or “optimizing”) Data Analysis (ODA) statistical paradigm, an optimization algorithm is first utilized to identify the model that explicitly maximizes predictive accuracy for the sample, and then the resulting […] The post best algorithm EVER !!!!!!!! appeared first on Statistical Modeling, Causal Inference, and…

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Optimization matchup: R’s glpkAPI vs Julia’s JuMP

December 6, 2016
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tl;dr: although I use R every day and love it, doing mathematical programming using Julia is much simpler and more flexible than anything I know that is currently available in R.Recently I have learned that Iain Dunning and Joey Huchette and Miles Lubi...

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Optimization matchup: R’s glpkAPI vs Julia’s JuMP

December 6, 2016
By

tl;dr: although I use R every day and love it, doing mathematical programming using Julia is much simpler and more flexible than anything I know that is currently available in R.Recently I have learned that Iain Dunning and Joey Huchette and Miles Lubi...

Read more »

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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Using Stan in an agent-based model: Simulation suggests that a market could be useful for building public consensus on climate change

December 5, 2016
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Using Stan in an agent-based model:  Simulation suggests that a market could be useful for building public consensus on climate change

Jonathan Gilligan writes: I’m writing to let you know about a preprint that uses Stan in what I think is a novel manner: Two graduate students and I developed an agent-based simulation of a prediction market for climate, in which traders buy and sell securities that are essentially bets on what the global average temperature […] The post Using Stan in an agent-based model: Simulation suggests that a market could…

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Cross-validation for time series

December 5, 2016
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Cross-validation for time series

I’ve added a couple of new functions to the forecast package for R which implement two types of cross-validation for time series.K-fold cross-validation for autoregression The first is regular k-fold cross-validation for autoregressive models. Al...

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Cross-validation for time series

December 5, 2016
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Cross-validation for time series

I’ve added a couple of new functions to the forecast package for R which implement two types of cross-validation for time series. K-fold cross-validation for autoregression The first is regular k-fold cross-validation for autoregressive models. Although cross-validation is sometimes not valid for time series models, it does work for autoregressions, which includes many machine learning […]

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Discover power laws by log-transforming data

December 5, 2016
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Discover power laws by log-transforming data

A recent issue of Astronomy magazine mentioned Kepler's third law of planetary motion, which states "the square of a planet's orbital period is proportional to the cube of its average distance from the Sun" (Astronomy, Dec 2016, p. 17). The article included a graph (shown at the right) that shows […] The post Discover power laws by log-transforming data appeared first on The DO Loop.

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Mighty oaks from little acorns grow

December 5, 2016
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Mighty oaks from little acorns grow

Eric Loken writes: Do by any chance remember the bogus survey that Augusta National carried out in 2002 to deflect criticism about not having any female members? I even remember this survey being ridiculed by ESPN who said their polls showed much more support for a boycott and sympathy with Martha Burke. Anyway, sure that’s […] The post Mighty oaks from little acorns grow appeared first on Statistical Modeling, Causal…

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Frustration with published results that can’t be reproduced, and journals that don’t seem to care

December 4, 2016
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Frustration with published results that can’t be reproduced, and journals that don’t seem to care

Thomas Heister writes: Your recent post about Per Pettersson-Lidbom frustrations in reproducing study results reminded me of our own recent experience that we had in replicating a paper in PLOSone. We found numerous substantial errors but eventually gave up as, frustratingly, the time and effort didn’t seem to change anything and the journal’s editors quite […] The post Frustration with published results that can’t be reproduced, and journals that don’t…

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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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So little information to evaluate effects of dietary choices

December 3, 2016
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So little information to evaluate effects of dietary choices

Paul Alper points to this excellent news article by Aaron Carroll, who tells us how little information is available in studies of diet and public health. Here’s Carroll: Just a few weeks ago, a study was published in the Journal of Nutrition that many reports in the news media said proved that honey was no […] The post So little information to evaluate effects of dietary choices appeared first on…

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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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