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06/07/2023
Causally Learning an Optimal Rework Policy
In manufacturing, rework refers to an optional step of a production proc...
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03/01/2023
Label Attention Network for sequential multi-label classification
Multi-label classification is a natural problem statement for sequential...
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08/24/2022
An Improved Bernstein-type Inequality for C-Mixing-type Processes and Its Application to Kernel Smoothing
There are many processes, particularly dynamic systems, that cannot be d...
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08/24/2022
Bernstein-type Inequalities and Nonparametric Estimation under Near-Epoch Dependence
The major contributions of this paper lie in two aspects. Firstly, we fo...
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07/10/2021
Machine Learning for Financial Forecasting, Planning and Analysis: Recent Developments and Pitfalls
This article is an introduction to machine learning for financial foreca...
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04/07/2021
DoubleML – An Object-Oriented Implementation of Double Machine Learning in Python
DoubleML is an open-source Python library implementing the double machin...
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03/17/2021
DoubleML – An Object-Oriented Implementation of Double Machine Learning in R
The R package DoubleML implements the double/debiased machine learning f...
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02/17/2021
Big Data meets Causal Survey Research: Understanding Nonresponse in the Recruitment of a Mixed-mode Online Panel
Survey scientists increasingly face the problem of high-dimensionality i...
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11/02/2020
Insights from Optimal Pandemic Shielding in a Multi-Group SEIR Framework
The COVID-19 pandemic constitutes one of the largest threats in recent d...
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04/03/2020
Uniform Inference in High-Dimensional Generalized Additive Models
We develop a method for uniform valid confidence bands of a nonparametri...
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12/30/2019
Adaptive Discrete Smoothing for High-Dimensional and Nonlinear Panel Data
In this paper we develop a data-driven smoothing technique for high-dime...
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12/11/2018
Closing the U.S. gender wage gap requires understanding its heterogeneity
In 2016, the majority of full-time employed women in the U.S. earned sig...
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09/13/2018
Valid Simultaneous Inference in High-Dimensional Settings (with the hdm package for R)
Due to the increasing availability of high-dimensional empirical applica...
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08/30/2018
A Self-Attention Network for Hierarchical Data Structures with an Application to Claims Management
Insurance companies must manage millions of claims per year. While most ...
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08/30/2018
Uniform Inference in High-Dimensional Gaussian Graphical Models
Graphical models have become a very popular tool for representing depend...
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12/31/2017
Estimation and Inference of Treatment Effects with L_2-Boosting in High-Dimensional Settings
Boosting algorithms are very popular in Machine Learning and have proven...
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12/20/2017
Transformation Models in High-Dimensions
Transformation models are a very important tool for applied statistician...
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02/10/2017
L_2Boosting for Economic Applications
In the recent years more and more high-dimensional data sets, where the ...
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08/01/2016
hdm: High-Dimensional Metrics
In this article the package High-dimensional Metrics (hdm) is introduced...
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03/05/2016
High-Dimensional Metrics in R
The package High-dimensional Metrics (hdm) is an evolving collection of ...
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02/29/2016
