A More Flexible Ljung-Box Test in SAS
Ljung-Box test is an important diagnostic to check if residuals from the time series model are independently distributed. In SAS / ETS module, it is easy to perform Ljung-Box with...continue reading.
Category: Statistics
SAS Macro Performing Breusch–Godfrey Test for Serial Correlation
%macro bgtest(data = , r = , x = , order = 1); ********************************************************************; * SAS MACRO PERFORMING BREUSCH-GODFREY TEST FOR SERIAL CORRELATION *; * BY FOLLOWING THE LOGIC OF...continue reading.
Optional stopping does not bias parameter estimates (if done correctly)
tl;dr: Optional stopping does not bias parameter estimates from a frequentist point of view if all studies are reported (i.e., no publication bias exists) and effect sizes are appropriately meta-analytically...continue reading.
Fold-change bar plots with “0” on y axis
I see it more and more frequently: bar plots which are supposed to illustrate the regulation of a gene in terms of “fold change”, which include a “0” on the...continue reading.
Vectorize a Function
Reading Time: 3 minutes I was recently working and decided to write a function to assist in the process. It assigns a label to a number based upon the value....continue reading.
Vectorize a Function
I was recently working and decided to write a function to assist in the process. It assigns a label to a number based upon the value. My first attempt worked,...continue reading.
Getting Started with Data Science: Storytelling with Data
Earlier this month, IBM Press and Pearson have published my book titled: Getting Started with Data Science: Making Sense of Data with Analytics. You can download sample pages, including a...continue reading.
Calculate the odd of winning Powerball in R
This Wednesday’s Powerball grand prize already climbed up to $1.5 BILLION. If you choose to cash out, it would be $930 million. And it keeps increasing… So, what’s the odd...continue reading.
Where Bagging Might Work Better Than Boosting
In the previous post (https://statcompute.wordpress.com/2016/01/01/the-power-of-decision-stumps), it was shown that the boosting algorithm performs extremely well even with a simple 1-level stump as the base learner and provides a better performance...continue reading.
The Power of Decision Stumps
A decision stump is the weak classification model with the simple tree structure consisting of one split, which can also be considered a one-level decision tree. Due to its simplicity,...continue reading.
Wind Resource Assessment
This is an article we recently published on “Renewable and Sustainable Energy Reviews”. It starts with a thorough review of the methods used for wind resource assessment: from algorithms based...continue reading.
Not so sweet sixteen!
In the world of big data and real-time analytics, Microsoft users are still living with the constraints of the bygone days of little data and basic numeracy.If you happen to...continue reading.
Curious about big data in Montreal?
Are you in Montreal and curious about big data? Well here is your chance to attend a session about the same at Concordia University on Tuesday, Nov. 03 at 6:00...continue reading.
Estimating Quasi-Poisson Regression with GLIMMIX in SAS
When modeling the frequency measure in the operational risk with regressions, most modelers often prefer Poisson or Negative Binomial regressions as best practices in the industry. However, as an alternative...continue reading.
Are Canadian newspapers painting false pictures with data?
The Canadian newspaper, Globe and Mail, is a leader in diction and style, but it may need improvement in the ‘grammar of graphics’.Globe’s recent depiction of metropolitan economic growth in...continue reading.
Stata embraces Bayesian statistics
Stata 14 has just been released. The new and big thing with version 14 is the introduction of Bayesian Statistics. A wide variety of new models can now be estimated...continue reading.
Modeling Count Time Series with tscount Package
The example below shows how to estimate a simple univariate Poisson time series model with the tscount package. While the model estimation is straightforward and yeilds very similar parameter estimates...continue reading.
Autoregressive Conditional Poisson Model – I
Modeling the time series of count outcome is of interest in the operational risk while forecasting the frequency of losses. Below is an example showing how to estimate a simple...continue reading.
Model Segmentation with Cubist
Cubist is a tree-based model with a OLS regression attached to each terminal node and is somewhat similar to mob() function in the Party package (https://statcompute.wordpress.com/2014/10/26/model-segmentation-with-recursive-partitioning). Below is a demonstrate...continue reading.
Flexible Beta Modeling
library(betareg) library(sas7bdat) df1 <- read.sas7bdat(‘lgd.sas7bdat’) df2 <- df1[df1$y < 1, ] fml <- as.formula(‘y ~ x2 + x3 + x4 + x5 + x6 | x3 + x4 | x1...continue reading.
