A reader, e.g. Mr. Wayne Zhang, of my previous post (https://statcompute.wordpress.com/2018/09/03/playing-map-and-reduce-in-r-by-group-calculation) made a good comment that “Why not use directly either Spark or H2O to derive such computations without involving...continue reading.
On Friday, while working on a project that I needed to union multiple data.frames with different column names, I realized that the base::rbind() function doesn’t take data.frames with different columns...continue reading.
Exact log-Bayes factors (log-BF) and H-factors (HF) of M1 against M2, computed for 100 independent samples (thin solid lines) of 1000 observations generated as i.i.d. N(1,1), under three increasingly vague...continue reading.
This blog post is one of a series highlighting specific images from my book Data Visualization: charts, maps and interactive … Morecontinue reading.
I am running a couple of online events through BayesCamp next month that might interest you if you want to … Morecontinue reading.
In the previous post (https://statcompute.wordpress.com/2018/09/03/playing-map-and-reduce-in-r-by-group-calculation), I’ve shown how to employ the MapReduce when calculating by-group statistics. Actually, the same Divide-n-Conquer strategy can be applicable to other use cases, one of...continue reading.
Clojure is such an interesting programming language that it can not only enhance our skill set but also change the way how we should write the program. After learning Clojure,...continue reading.
In the previous post (https://statcompute.wordpress.com/2018/08/26/adjacent-categories-and-continuation-ratio-logit-models-for-ordinal-outcomes), we’ve shown alternative models for ordinal outcomes in addition to commonly used Cumulative Logit models under the proportional odds assumption, which are also known as...continue reading.
In the previous post (https://statcompute.wordpress.com/2018/01/28/modeling-lgd-with-proportional-odds-model), I’ve shown how to estimate a standard Cumulative Logit model with the ordinal::clm function and its use case in credit risk models. To better a...continue reading.
The analysis of high frequency stock transactions has played an important role in the algorithmic trading and the result can be used to monitor stock movements and to develop trading...continue reading.