Posts Tagged ‘ statistics ’

Bias/variance tradeoff as gamesmanship

October 30, 2014
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Bias/variance tradeoff as gamesmanship

Continuing our series of reading out loud from a single page of a statistics book we look at page 224 of the 1972 Dover edition of Leonard J. Savage’s “The Foundations of Statistics.” On this page we are treated to an example attributed to Leo A. Goodman in 1953 that illustrates how for normally distributed […] Related posts: Automatic bias correction doesn’t fix omitted variable bias Reading the Gauss-Markov theorem…

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Improved graphs of car crash stats

October 29, 2014
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Improved graphs of car crash stats

Last week, Mona Chalabi wrote an interesting post on car crash statistics by state, at fivethirtyeight.com. I didn’t like the figures so much, though. There were a number of them like this: I’m giving a talk today about data visualization [slides | github], and I thought this would make a good example, so I spent […]

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Calculating the sum or mean of a numeric (continuous) variable by a group (categorical) variable in SAS

Calculating the sum or mean of a numeric (continuous) variable by a group (categorical) variable in SAS

Introduction A common task in data analysis and statistics is to calculate the sum or mean of a continuous variable.  If that variable can be categorized into 2 or more classes, you may want to get the sum or mean for each class. This sounds like a simple task, yet I took a surprisingly long time […]

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reliable ABC model choice via random forests

October 28, 2014
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reliable ABC model choice via random forests

After a somewhat prolonged labour (!), we have at last completed our paper on ABC model choice with random forests and submitted it to PNAS for possible publication. While the paper is entirely methodological, the primary domain of application of ABC model choice methods remains population genetics and the diffusion of this new methodology to […]

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Blue Bonnet Bayes

October 28, 2014
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Blue Bonnet Bayes

Blue Bonnet™ used to run commercials with the jingle “Everything’s better with Blue Bonnet on it.” Maybe they still do. Perhaps in reaction to knee-jerk antipathy toward Bayesian methods, some statisticians have adopted knee-jerk enthusiasm for Bayesian methods. Everything’s better with Bayesian analysis on it. Bayes makes it better, like a little dab of margarine […]

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Probable Points and Credible Intervals, Part 1

October 26, 2014
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Probable Points and Credible Intervals, Part 1

After having broken the Bayesian eggs and prepared your model in your statistical kitchen the main dish is the posterior. The posterior is the posterior is the posterior, given the model and the data it contains all the information you need and anyth...

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3 YEARS AGO: MONTHLY MEMORY LANE

October 25, 2014
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3 YEARS AGO: MONTHLY MEMORY LANE

MONTHLY MEMORY LANE: 3 years ago: October 2011 (I mark in red 3 posts that seem most apt for general background on key issues in this blog*) (10/3) Part 2 Prionvac: The Will to Understand Power (10/4) Part 3 Prionvac: How the Reformers Should Have done Their Job (10/5) Formaldehyde Hearing: How to Tell the Truth With Statistically Insignificant Results (10/7) Blogging […]

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How well does sample range estimate range?

October 25, 2014
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How well does sample range estimate range?

I’ve been doing some work with Focused Objective lately, and today the following question came up in our discussion. If you’re sampling from a uniform distribution, how many samples do you need before your sample range has an even chance of covering 90% of the population range? This is a variation on a problem I’ve […]

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Feller’s shoes and Rasmus’ socks [well, Karl's actually...]

October 23, 2014
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Feller’s shoes and Rasmus’ socks [well, Karl's actually...]

Yesterday, Rasmus Bååth [of puppies' fame!] posted a very nice blog using ABC to derive the posterior distribution of the total number of socks in the laundry when only pulling out orphan socks and no pair at all in the first eleven draws. Maybe not the most pressing issue for Bayesian inference in the era […]

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Singular Spectrum Analysis in Excel

October 23, 2014
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Singular Spectrum Analysis (SSA) is a technique for analysing time series. The method is relatively simple to implement and relies on applying some linear algebra. There is no requirement to do any pre-processing before applying SSA so practical implem...

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