The team at EViews has put together a great set of videos that highlight some of the new features in EViews 9.You can find them here, and I strongly recommend them.© 2015, David E. Giles

As I wrote a couple years ago: Statistics does not require randomness. The three essential elements of statistics are measurement, comparison, and variation. Randomness is one way to supply variation, and it’s one way to model variation, but it’s not necessary. Nor is it necessary to have “true” randomness (of the dice-throwing or urn-sampling variety) […] The post What’s the most important thing in statistics that’s not in the textbooks?…

MONTHLY MEMORY LANE: 3 years ago: March 2012. I mark in red three posts that seem most apt for general background on key issues in this blog* (Posts that are part of a “unit” or a group of “U-Phils” count as one.) This new feature, appearing the last week of each month, began at the blog’s 3-year anniversary in Sept, 2014. *excluding those recently reblogged. April […]

A few days ago, I was asked if we should spend a lot of time to choose the distribution we use, in GLMs, for (actuarial) ratemaking. On that topic, I usually claim that the family is not the most important parameter in the regression model. Consider the following dataset > db <- data.frame(x=c(1,2,3,4,5),y=c(1,2,4,2,6)) > plot(db,xlim=c(0,6),ylim=c(-1,8),pch=19) To visualize a regression model, use the following code > nd=data.frame(x=seq(0,6,by=.1)) > add_predict = function(reg){…

I just read this charming article by Lee Wilkinson’s brother on a mathematician named Yitang Zhang. Zhang recently gained some fame after recently proving a difficult theorem, and he seems to be a quite unusual, but likable, guy. What I liked about Wilkinson’s article is how it captured Zhang’s eccentricities with affection but without condescension. […] The post Eccentric mathematician appeared first on Statistical Modeling, Causal Inference, and Social Science.

Mon: Eccentric mathematician Tues: What’s the most important thing in statistics that’s not in the textbooks? Wed: Carl Morris: Man Out of Time [reflections on empirical Bayes] Thurs: “The general problem I have with noninformatively-derived Bayesian probabilities is that they tend to be too strong.” Fri: Good, mediocre, and bad p-values Sat: Which of these […] The post On deck this week appeared first on Statistical Modeling, Causal Inference, and…

Sometimes different communities use the same name for different objects. To a soldier, "boots" are rugged, heavy, high-top foot coverings. To a soccer (football) player, "boots" are lightweight cleats. So it is with the term "waterfall plot." To researchers in the medical field, a "waterfall plot" is a sorted bar […] The post Create a cascade chart in SAS appeared first on The DO Loop.

Last week, I had the pleasure of attending the CHI 2015 conference in Seoul, South Korea. CHI technically stands for Computer-Human Interaction, but it has become a name rather than an acronym in recent years. And CHI’s scope is very broad, it covers many areas that are not strictly part of HCI (Human-Computer Interaction – … Continue reading Conference Report: CHI 2015

Dylan Small writes: The conference will take place May 20-21 (with a short course on May 19th) and the web site for the conference is here. The deadline for submitting a poster title for the poster session is this Friday. Junior researchers (graduate students, postdoctoral fellows, and assistant professors) whose poster demonstrates exceptional research will […] The post This year’s Atlantic Causal Inference Conference: 20-21 May appeared first on Statistical…

In Python, sklearn (scikit-learn)'s DecisionTree example uses pydot for plotting the generated tree: @here.But for Python 3, pydot has some issues with the string from dot_data.getvalue(), for example it will report "TypeError: startswith first arg mus...

We've just arxived our paper on efficient computation for the Expected Value of Partial Perfect Information (EVPPI) based on SPDE-INLA. The EVPPI is a decision-theoretic measure of the impact of uncertainty in some of the parameters in a mode...

This is an oldie but a goodie. Donna Towns writes: I am wondering if you could help me solve an ongoing debate? My colleagues and I are discussing (disagreeing) on the ability of a researcher to analyze information on a population. My colleagues are sure that a researcher is unable to perform statistical analysis on […] The post Statistical analysis on a dataset that consists of a population appeared first…

Another powerful procedure of SAS, my favorite one, that I would like to share is the PROC IML (Interactive Matrix Language). This procedure treats all objects as a matrix, and is very useful for doing scientific computations involving vectors and matrices. To get started, we are going to demonstrate and discuss the following: Creating and Shaping Matrices;Matrix Query;Subscripts;Descriptive Statistics;Set Operations;Probability Functions and Subroutine;Linear Algebra;Reading and Creating Data;Above outline is based…