Mapping data using R and leaflet
The R language provides many different tools for creating maps and adding data to them. I’ve been using the leaflet package at work recently, so I thought I’d provide a...continue reading.
Category: Statistics
Metropolis-in-Gibbs Sampling and Runtime Analysis with Profviz
First off, here are the previous posts in my Bayesian sampling series: Bayesian Simple Linear Regression with Gibbs Sampling in R Blocked Gibbs Sampling in R for Bayesian Multiple Linear...continue reading.
The BayesianTools R package with general-purpose MCMC and SMC samplers for Bayesian statistics
This is a somewhat belated introduction of a package that we published on CRAN at the beginning of the year already, but I hadn’t found the time to blog about this earlier....continue reading.
Last Day for Stanton Predictions
This week I’ve been looking at two models in R that are attempting to predict whether Giancarlo Stanton would break Roger Maris’ mark of 61 home runs in a season....continue reading.
Monotonic WoE Binning for LGD Models
While the monotonic binning algorithm has been widely used in scorecard and PD model (Probability of Default) developments, the similar idea can be generalized to LGD (Loss Given Default) models....continue reading.
Hold the Phones . . . Stanton’s Alive Again
Sometimes, events move faster than we predict them. This is one of the things that makes statistics as much of an art as a science. Last night, Giancarlo Stanton hit...continue reading.
Going, Going . . . 1
Two posts today with similar themes. Time is running out. First, time is running out for Giancarlo Stanton. His bat has been very silent this week so far. The Marlins...continue reading.
The Battle of Bayesian Home Run Models
The regular Major League Baseball season is coming to an end. Next week, we move into the playoffs and eventually the World Series. However, we have a nice statistical modeling...continue reading.
Granular Monotonic Binning in SAS
In the post (https://statcompute.wordpress.com/2017/06/15/finer-monotonic-binning-based-on-isotonic-regression), it is shown how to do a finer monotonic binning with isotonic regression in R. Below is a SAS macro implementing the monotonic binning with the...continue reading.
Model Non-Negative Numeric Outcomes with Zeros
As mentioned in the previous post (https://statcompute.wordpress.com/2017/06/29/model-operational-loss-directly-with-tweedie-glm/), we often need to model non-negative numeric outcomes with zeros in the operational loss model development. Tweedie GLM provides a convenient interface to...continue reading.
Mathpy 0.3.0 Released!
I am excited to announce the release of mathpy 0.3.0! This release adds a ton of Excel UDFs including many new statistical and number-theoretic functions, several random number generators and…...continue reading.
Blocked Gibbs Sampling in R for Bayesian Multiple Linear Regression
In a previous post, I derived and coded a Gibbs sampler in R for estimating a simple linear regression. In this post, I will do the same for multivariate linear...continue reading.
Variable Selection with Elastic Net
LASSO has been a popular algorithm for the variable selection and extremely effective with high-dimension data. However, it often tends to “over-regularize” a model that might be overly compact and...continue reading.
Mathpy 0.2.0 Released!
My Python library, mathpy, a collection of mathematical and statistical functions with Excel integration, has a new release! Version 0.2.0 introduces a ton of additional mathematical and statistical functions have…...continue reading.
Introducing ‘powerlmm’ an R package for power calculations for longitudinal multilevel models
Over the years I’ve produced quite a lot of code for power calculations and simulations of different longitudinal linear mixed models. Over the summer I bundled together these calculations for...continue reading.
DART: Dropout Regularization in Boosting Ensembles
The dropout approach developed by Hinton has been widely employed in deep learnings to prevent the deep neural network from overfitting, as shown in https://statcompute.wordpress.com/2017/01/02/dropout-regularization-in-deep-neural-networks. In the paper http://proceedings.mlr.press/v38/korlakaivinayak15.pdf, the...continue reading.
Model Operational Losses with Copula Regression
In the previous post (https://statcompute.wordpress.com/2017/06/29/model-operational-loss-directly-with-tweedie-glm), it has been explained why we should consider modeling operational losses for non-material UoMs directly with Tweedie models. However, for material UoMs with significant losses,...continue reading.
Bayesian Simple Linear Regression with Gibbs Sampling in R
Many introductions to Bayesian analysis use relatively simple didactic examples (e.g. making inference about the probability of success given bernoulli data). While this makes for a good introduction to Bayesian...continue reading.
Monty Hall Problem – How Randomness Rules Our World and Why We Cannot See It
Ever since I read about Monty Hall problem in “The Drunkard’s Walk: How Randomness Rules Our Lives” book by Leonard Mlodinow from of the California Institute of Technology, I always wanted...continue reading.
Bayesian model checking via posterior predictive simulations (Bayesian p-values) with the DHARMa package
As I said before, I firmly side with Andrew Gelman (see e.g. here) in that model checking is dangerously neglected in Bayesian practice. The philosophical criticism against “rejecting” models (double-using data...continue reading.
