Answer here (courtesy of Kaiser Fung).The post What makes a statistician look like a hero? appeared first on Statistical Modeling, Causal Inference, and Social Science.

Answer here (courtesy of Kaiser Fung).The post What makes a statistician look like a hero? appeared first on Statistical Modeling, Causal Inference, and Social Science.

Thomas Lumley writes: The Herald has a story about hazards of coffee. The picture caption says Men who drink more than four cups a day are 56 per cent more likely to die. which is obviously not true: deaths, as we’ve observed before, are fixed at one per customer. The story says It’s not that people […]The post Is coffee a killer? I don’t think the effect is as high as…

The title of this book Informative Hypotheses somehow put me off from the start: the author, Hebert Hoijtink, seems to distinguish between informative and uninformative (deformative? disinformative?) hypotheses. Namely, something like H0: μ1=μ2=μ3=μ4 is “very informative” and unrealistic, and the alternative Ha is completely uninformative, while the “alternative null” H1: μ1<μ2=μ3<μ4 is informative. (Hence the < […]

A recently arxived paper by Pier Bissiri, Chris Holmes and Steve Walker piqued my curiosity about “pseudo-Bayesian” approaches, that is, statistical approaches based on a pseudo-posterior: where is some pseudo-likelihood. Pier, Chris and Steve use in particular where is some empirical risk function. A good example is classification; then could be the proportion of properly […]

David Kaplan writes: I came across your paper “Understanding Posterior Predictive P-values”, and I have a question regarding your statement “If a posterior predictive p-value is 0.4, say, that means that, if we believe the model, we think there is a 40% chance that tomorrow’s value of T(y_rep) will exceed today’s T(y).” This is perfectly […]The post Understanding posterior p-values appeared first on Statistical Modeling, Causal Inference, and Social Science.

Like many other computer packages, SAS can produce a contour plot that shows the level sets of a function of two variables. For example, I've previously written blogs that use contour plots to visualize the bivariate normal density function and to visualize the cumulative normal distribution function. However, sometimes you [...]

A couple of announcements: First: A message from Jeff Leek: “We are hosting an “unconference” on Google Hangouts. We got some really amazing speakers to talk about the future of statistics. I wonder if you could help advertise the unconference on your blogs. Here is our post: http://simplystatistics.org/2013/09/17/announcing-the-simply-statistics-unconference-on-the-future-of-statistics-futureofstats/ and the sign up page: https://plus.google.com/events/cd94ktf46i1hbi4mbqbbvvga358 We […]

The last post about Mayo’s Severity Principle got me thinking about that xkcd cartoon which generated so much hate-and-discontent among Frequentists. I didn’t care for it because all Frequentists can say in response is “we’re not that dumb”; ...

Inspired by the Future of Statistical Sciences Workshop (an official event which will be full of middle-aged guys talking about the future), Jeff Leek and Roger Peng have organized a mini-conference in the form of a Google hangout featuring a bunch of youngsters. Seems like a good idea to me! Their particular focus will be […]The post Online conference for young statistics researchers appeared first on Statistical Modeling, Causal Inference,…

Sign up here! We here at Simply Statistics are pumped about the Statistics 2013 Future of Statistical Sciences Workshop (Nov. 11-12). It is a great time to be a statistician and discussing the future of our discipline is of utmost importance … Continue reading →

X writes: This paper discusses the dual interpretation of the Jeffreys– Lindley’s paradox associated with Bayesian posterior probabilities and Bayes factors, both as a differentiation between frequentist and Bayesian statistics and as a pointer to the difficulty of using improper priors while testing. We stress the considerable impact of this paradox on the foundations of […]The post Christian Robert on the Jeffreys-Lindley paradox; more generally, it’s good news when philosophical…

Lecture 5: Using multiple functions to solve multiple problems; to sub-divide awkward problems into more tractable ones; to re-use solutions to recurring problems. Value of consistent interfaces for functions working with the same object, or doing si...

In which we practice the arts of writing functions and of estimating distributions, while contemplating just how little room there is in the heart of a cat. Lab Introduction to Statistical Computing

In which we see how to minimize the mean squared error when there are two parameters, in the process learning about writing functions, decomposing problems into smaller steps, testing the solutions to the smaller steps, and minimization by gradient de...

Lecture 6: Top-down design is a recursive heuristic for solving problems by writing functions: start with a big-picture view of the problem; break it into a few big sub-problems; figure out how to integrate the solutions to each sub-problem; and then ...

I’ve had several emails recently asking how to forecast daily data in R. Unless the time series is very long, the easiest approach is to simply set the frequency attribute to 7. y <- ts(x, frequency=7) Then any of the usual time series forecasting methods should produce reasonable forecasts. For example library(forecast) fit <- ets(y) fc <- forecast(fit) plot(fc) When the time series is long enough to take in more…