Articles by Iegor Rudnytskyi

???? R Coding Style Guide

January 13, 2019 | Iegor Rudnytskyi

Language is a tool that allows human beings to interact and communicate with each other. The clearer we express ourselves, the better the idea is transferred from our mind to the other. The same applies to programming languages: concise, clear and consistent codes are easier to read and edit. It ...
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???? Project-oriented workflow

January 6, 2019 | Iegor Rudnytskyi

Be honest with yourself, how many times have you wanted to restart an on-going project from scratch throwing away the current folder? Or how many times have you had to rename files and adjust folder structure to make your project simple and clear? Not to mention, all these thousands of ...
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???? Simulating Poisson process (part 2)

March 12, 2018 | Iegor Rudnytskyi

In previous post we discussed two common methods of Poisson process simulation. The reason why this trivial problem was of my interest is the fact that this is simplification of a larger scale problem of a classical ruin process. Let me remind that I focus on an extenssion of Cramé...
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???? Simulating Poisson process (part 1)

March 8, 2018 | Iegor Rudnytskyi

A couple of weeks ago a colleague of mine asked me for a help to estimate Gerber-Shiu function by Monte-Carlo methods. The function is used in ruin theory for risk processes. One can think about this function as of equialence to a moment generating function. That is if the function ...
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???? Multinomial regression in R

January 23, 2018 | Iegor Rudnytskyi

In my current project on Long-term care at some point we were required to use a regression model with multinomial responses. I was very surprised that in contrast to well-covered binomial GLM for binary response case, multinomial case is poorly described. Surely, there are half-dozen packages overlapping each other, however, ... [Read more...]

???? Dortmund real estate market analysis: tree-based methods

September 27, 2017 | Iegor Rudnytskyi

In pervious posts traditional regression models were fitted to real estate data. In this post tree-based models, namely random forests and gradient boosting, are trained to predict prices of the rent. These methods typically outperform traditional regression models yielding smaller errors. Furthermore, tree-based methods are much more robust to overfitting, ...
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