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.
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.
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.
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.
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.
Trend of US Immigration Enforcement In the coming months I’ll be digging into the immigration enforcement data posted on data.world. I encourage anyone to take this data and either add...continue reading.
Click here to explore the data for yourself First off — I’ll admit that was my poor attempt at a click-bait title. But if you’re still reading the next paragraph,...continue reading.
Using the Google Vision API in R Utilizing RoogleVision After doing my post last month on OpenCV and face detection, I started looking into other algorithms used for pattern detection...continue reading.
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.
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.
In the development of operational loss forecasting models, the Frequency-Severity modeling approach, which the frequency and the severity of a Unit of Measure (UoM) are modeled separately, has been widely...continue reading.
Below is an example showing how to fit a Generalized Linear Model with H2O in R. The output is much more comprehensive than the one generated by the generic R...continue reading.
In the development of operational loss models, it is important to identify which distribution should be used to model operational risk measures, e.g. frequency and severity. For instance, why should...continue reading.
Abstract We provide an interpretation for the confidence interval for a binomial proportion hidden as the transcript of an hypothetical statistical consulting session. This work is licensed under a Creative...continue reading.
Abstract If your tap water suddenly becomes brown while authorities claim everything is okay, you start to worry. Langkjær-Bain (2017) tells the Flint Water Crisis story from a statistical viewpoint:...continue reading.
In my early post (https://statcompute.wordpress.com/2017/01/22/monotonic-binning-with-smbinning-package/), I wrote a monobin() function based on the smbinning package by Herman Jopia to improve the monotonic binning algorithm. The function works well and provides...continue reading.
A guest post by Carsten F. Dormann Over the last years, I have been using null models more often than I liked. I had to, when there was no other way...continue reading.
tl;dr: Publication bias and p-hacking can dramatically inflate effect size estimates in meta-analyses. Many methods have been proposed to correct for such bias and to estimate the underlying true effect....continue reading.
Medicare Data – R Shiny App About two weeks ago I created an extremely rough version of an R Shiny Application surrounding Medicare data. Right after publishing the blog post,...continue reading.
Hello R community. if you’re up for some fun tinkering with a Shiny App please join me on a new project. I would love to see some collaboration in designing...continue reading.
Artificial intelligence / Deep neural networks / Keras / R / R News / Spark
April 22, 2024
GIS / R / R News / Shiny / Visualization
Interactive visualization of geospatial data with R Shiny
April 17, 2021
