# Posts Tagged ‘ statistics ’

## Probability approximations

January 28, 2015
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This week’s resource post lists notes on probability approximations. Do we even need probability approximations anymore? They’re not as necessary for numerical computation as they once were, but they remain vital for understanding the behavior of probability distributions and for theoretical calculations. Textbooks often leave out details such as quantifying the error when discussion approximations. The […]

## 3 YEARS AGO: (JANUARY 2012) MEMORY LANE

January 28, 2015
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MONTHLY MEMORY LANE: 3 years ago: January 2012. I mark in red three posts that seem most apt for general background on key issues in this blog. January 2012 (1/3) Model Validation and the LLP-(Long Playing Vinyl Record) (1/8) Don’t Birnbaumize that Experiment my Friend* (1/10) Bad-Faith Assertions of Conflicts of Interest?* (1/13) U-PHIL: “So you want to do a philosophical analysis?” (1/14) “You May Believe You […]

## Check your return types when modeling in R

January 27, 2015
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Just a warning: double check your return types in R, especially when using different modeling packages. We consider ourselves pretty familiar with R. We have years of experience, many other programming languages to compare R to, and we have taken Hadley Wickham’s Master R Developer Workshop (highly recommended). We already knew R’s predict function is … Continue reading Check your return types when modeling in R → Related posts: R…

## More data, less accuracy

January 27, 2015
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Statistical methods should do better with more data. That’s essentially what the technical term “consistency” means. But with improper numerical techniques, the the numerical error can increase with more data, overshadowing the decreasing statistical error. There are three ways Bayesian posterior probability calculations can degrade with more data: Polynomial approximation Missing the spike Underflow Elementary numerical integration algorithms, […]

## Trial on Anil Potti’s (clinical) Trial Scandal Postponed Because Lawyers Get the Sniffles (updated)

January 27, 2015
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Trial in Medical Research Scandal Postponed By Jay Price DURHAM, N.C. — A judge in Durham County Superior Court has postponed the first civil trial against Duke University by the estate of a patient who had enrolled in one of a trio of clinical cancer studies that were based on bogus science. The case is […]

## the density that did not exist…

January 26, 2015
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$the density that did not exist…$

On Cross Validated, I had a rather extended discussion with a user about a probability density as I thought it could be decomposed in two manageable conditionals and simulated by Gibbs sampling. The first component led to a Gumbel like density wirh y being restricted to either (0,1) or (1,∞) depending on β. The density […]

## What do these share in common: m&ms, limbo stick, ovulation, Dale Carnegie? Sat night potpourri

January 25, 2015
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Here’s the follow-up to my last (reblogged) post. initially here. My take hasn’t changed much from 2013. Should we be labeling some pursuits “for entertainment only”? Why not? (See also a later post on the replication crisis in psych.) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ I had said I would label as pseudoscience or questionable science any enterprise that regularly permits the kind of […]

## RSS feeds for statistics and related journals

January 23, 2015
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I’ve now resurrected the collection of research journals that I follow, and set it up as a shared collection in feedly. So anyone can easily subscribe to all of the same journals, or select a subset of them, to follow on feedly. There are about 90 journals on the list, mostly in statistics, but some from machine […]

## Assertive R programming in dplyr/magrittr pipelines

January 23, 2015
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A lot of my job–and side projects, for that matter–involve running R scripts on updates of open government data. While I’m infinitely grateful to have access to any interesting open datasets in the first place, I can’t ignore that dealing… Continue reading →

## Sequential Monte Carlo 2015 workshop

January 22, 2015
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An announcement for the SMC 2015 workshop: Sequential Monte Carlo methods (also known as particle filters) have revolutionized the on-line and off-line analysis of data in fields as diverse as target tracking, computer vision, financial modelling, brain imagery, or population ecology. Their popularity stems from the fact that they have made possible to solve numerically many […]