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.
Category: Rstat
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.
Speeding up the Cluster Bootstrap in R
Back in January 2013 I wrote a blog post showing how to implement a basic cluster/block bootstrap in R. One drawback of the cluster bootstap is the length of time...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.
How Predictable is the English Premier League?
The reason why football is so exciting is uncertainty. The outcome of any match or league is unknown, and you get to watch the action unfold without knowing what’s going...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.
Lakers Lent: Chuck should have fasted sooner and Historical Win Trajectories
For the 2015 NBA season, the only exciting Lakers news is the return of the Kobe show and Charles Barkley’s Lakers Lent.The Lakers started the season with 0 wins and...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.
Estimating a Beta Regression with The Variable Dispersion in R
pkgs <- c(‘sas7bdat’, ‘betareg’, ‘lmtest’) lapply(pkgs, require, character.only = T) df1 <- read.sas7bdat("lgd.sas7bdat") df2 <- df1[which(df1$y < 1), ] xvar <- paste("x", 1:7, sep = ”, collapse = " +...continue reading.
Summarize statistics by Groups in R & R Commander
R is great at accomplishing complex tasks. Doing simple things with R though takes some effort. Consider the simple task of producing summary statistics for continuous variables over some factor...continue reading.
Modeling in R with Log Likelihood Function
Similar to NLMIXED procedure in SAS, optim() in R provides the functionality to estimate a model by specifying the log likelihood function explicitly. Below is a demo showing how to...continue reading.
Fractional Logit Model with Python
In [1]: import pandas as pd In [2]: import statsmodels.api as sm In [3]: data = pd.read_table(‘/home/liuwensui/Documents/data/csdata.txt’) In [4]: Y = data.LEV_LT3 In [5]: X = sm.add_constant(data[[‘COLLAT1’, ‘SIZE1’, ‘PROF2’, ‘LIQ’,...continue reading.
