Posts Tagged ‘ statistics ’

Getting Credit (or blame) for Something You Didn’t Do (BP oil spill)

April 20, 2014
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Getting Credit (or blame) for Something You Didn’t Do (BP oil spill)

  Four years ago, many of us were glued to the “spill cam” showing, in real time, the gushing oil from the April 20, 2010 explosion sinking the Deepwater Horizon oil rig in the Gulf of Mexico, killing 11, and spewing oil until July 15.(Remember junk shots, top kill, blowout preventers?)[1] The EPA has lifted its gulf drilling ban on BP just […]

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Monotonicity of EM Algorithm Proof

April 19, 2014
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Here the monotonicity of the EM algorithm is established. $$ f_{o}(Y_{o}|\theta)=f_{o,m}(Y_{o},Y_{m}|\theta)/f_{m|o}(Y_{m}|Y_{o},\theta)$$ $$ \log L_{o}(\theta)=\log L_{o,m}(\theta)-\log f_{m|o}(Y_{m}|Y_{o},\theta) \label{eq:loglikelihood} $$ where \( L_{o}(\theta)\) is the likelihood under the observed data and \(L_{o,m}(\theta)\) is the likelihood under the complete data. Taking the expectation of the second line with respect to the conditional distribution of \(Y_{m}\) given \(Y_{o}\) and […] The post Monotonicity of EM Algorithm Proof appeared first on Lindons Log.

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Why the “sample from infinite population” metaphor has been such a disaster for reproducible science.

April 19, 2014
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The “sampling from an infinite population” metaphor beloved by statisticians of all types is a disaster for reproducible science. To explain why I’ll show what sampling from a finite population has going for it that’s not there ...

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Old tails: a crude power law fit on ebook sales

April 18, 2014
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Old tails: a crude power law fit on ebook sales

We use R to take a very brief look at the distribution of e-book sales on Amazon.com. Recently Hugh Howey shared some eBook sales data spidered from Amazon.com: The 50k Report. The data is largely a single scrape of statistics about various anonymized books. Howey’s analysis tries to break sales down by declared category and […] Related posts: Sample size and power for rare events Living in A Lognormal World…

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An overused chart, why it fails, and how to fix it

April 17, 2014
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An overused chart, why it fails, and how to fix it

Reader and tipster Chris P. found this "death spiral" chart dizzying (link). It's one of those charts that has conceptual appeal but does not do the data justice. As the name implies, the designer has a strong message, that the...

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The horrible confusion between different entropies explained in a way that answers: Where do likelihoods and priors come from?

April 16, 2014
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Here I derive a simple formula for probability distributions general enough for Statistical Mechanics and Classical Statistics in which the roles, meanings, and interpretations between the Information Entropy and Boltzmann’s Entropy are as clear ...

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Errors on percentage errors

April 16, 2014
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Errors on percentage errors

The MAPE (mean absolute percentage error) is a popular measure for forecast accuracy and is defined as     where denotes an observation and denotes its forecast, and the mean is taken over . Armstrong (1985, p.348) was the first (to my knowledge) to point out the asymmetry of the MAPE saying that “it has a bias favoring estimates that are below the actual values”. A few years later, Armstrong…

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A. Spanos: Jerzy Neyman and his Enduring Legacy

April 16, 2014
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A. Spanos: Jerzy Neyman and his Enduring Legacy

A Statistical Model as a Chance Mechanism Aris Spanos  Jerzy Neyman (April 16, 1894 – August 5, 1981), was a Polish/American statistician[i] who spent most of his professional career at the University of California, Berkeley. Neyman is best known in statistics for his pioneering contributions in framing the Neyman-Pearson (N-P) optimal theory of hypothesis testing […]

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Video Tutorial – Rolling 2 Dice: An Intuitive Explanation of The Central Limit Theorem

Video Tutorial – Rolling 2 Dice: An Intuitive Explanation of The Central Limit Theorem

According to the central limit theorem, if random variables, , are independent and identically distributed, is sufficiently large, then the distribution of their sample mean, , is approximately normal, and this approximation is better as increases. One of the most remarkable aspects of the central limit theorem (CLT) is its validity for any parent distribution of […]

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Timid medical research

April 15, 2014
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Cancer research is sometimes criticized for being timid. Drug companies run enormous trials looking for small improvements. Critics say they should run smaller trials and more of them. Which side is correct depends on what’s out there waiting to be…Read more ›

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