In manufacturing, rework refers to an optional step of a production proc...
Multi-label classification is a natural problem statement for sequential...
There are many processes, particularly dynamic systems, that cannot be
d...
The major contributions of this paper lie in two aspects. Firstly, we fo...
This article is an introduction to machine learning for financial
foreca...
DoubleML is an open-source Python library implementing the double machin...
The R package DoubleML implements the double/debiased machine learning
f...
Survey scientists increasingly face the problem of high-dimensionality i...
The COVID-19 pandemic constitutes one of the largest threats in recent
d...
We develop a method for uniform valid confidence bands of a nonparametri...
In this paper we develop a data-driven smoothing technique for
high-dime...
In 2016, the majority of full-time employed women in the U.S. earned
sig...
Due to the increasing availability of high-dimensional empirical applica...
Insurance companies must manage millions of claims per year. While most ...
Graphical models have become a very popular tool for representing
depend...
Boosting algorithms are very popular in Machine Learning and have proven...
Transformation models are a very important tool for applied statistician...
In the recent years more and more high-dimensional data sets, where the
...
In this article the package High-dimensional Metrics (hdm) is
introduced...
The package High-dimensional Metrics (hdm) is an evolving
collection of ...
Boosting is one of the most significant developments in machine learning...