Machine Learning: An Applied Econometric Approach
Sendhil Mullainathan,Jann Spiess +1 more
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TLDR
This work presents a way of thinking about machine learning that gives it its own place in the econometric toolbox, and aims to make them conceptually easier to use by providing a crisper understanding of how these algorithms work, where they excel, and where they can stumble.Abstract:
Machines are increasingly doing “intelligent” things. Face recognition algorithms use a large dataset of photos labeled as having a face or not to estimate a function that predicts the pre...read more
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Book ChapterDOI
Feedforward Neural Networks
TL;DR: This chapter provides a more in-depth description of supervised learning, deep learning, and neural networks—presenting the foundational mathematical and statistical learning concepts and explaining how they relate to real-world examples in trading, risk management, and investment management.
Journal ArticleDOI
Agricultural loan delinquency prediction using machine learning methods
TL;DR: The results show that ensemble learning methods have the best performance in prediction accuracy, with improvement of 26 percentage points at most and that the Naïve Bayes classifier is the best method to maintain the lowest cost from false predictions when the failure of identifying potentially high-risk loans is very costly.
Journal ArticleDOI
Predicting salvage laryngectomy in patients treated with primary nonsurgical therapy for laryngeal squamous cell carcinoma using machine learning
Joshua B. Smith,Matthew Shew,Omar A. Karadaghy,Rohit Nallani,Kevin J. Sykes,Gregory N Gan,Jason A. Brant,Andrés M. Bur +7 more
TL;DR: Machine learning (ML) algorithms may predict patients who will require salvage total laryngectomy (STL) after primary radiotherapy with or without chemotherapy forLaryngeal squamous cell carcinoma (SCC).
Journal ArticleDOI
Machine learning portfolios with equal risk contributions: Evidence from the Brazilian market
TL;DR: In this paper , the authors investigated the use of machine learning (ML) to forecast stock returns in the Brazilian market using a rich proprietary dataset and showed that an Equal Risk Contribution (ERC) approach significantly improves risk-adjusted returns.
Journal ArticleDOI
Decomposition of Improvements in Infant Mortality in Asian Developing Countries Over Three Decades
TL;DR: In this article, the authors examined the factors contributing to the improvement in infant survival in their first year in six Asian countries: Bangladesh, India, Indonesia, Nepal, Pakistan, and the Philippines.
References
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Journal ArticleDOI
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
TL;DR: The Elements of Statistical Learning: Data Mining, Inference, and Prediction as discussed by the authors is a popular book for data mining and machine learning, focusing on data mining, inference, and prediction.
Journal ArticleDOI
Problems with Instrumental Variables Estimation when the Correlation between the Instruments and the Endogenous Explanatory Variable is Weak
TL;DR: In this article, the use of instruments that explain little of the variation in the endogenous explanatory variables can lead to large inconsistencies in the IV estimates even if only a weak relationship exists between the instruments and the error in the structural equation.
Journal Article
On Model Selection Consistency of Lasso
Peng Zhao,Bin Yu +1 more
TL;DR: It is proved that a single condition, which is called the Irrepresentable Condition, is almost necessary and sufficient for Lasso to select the true model both in the classical fixed p setting and in the large p setting as the sample size n gets large.
Journal ArticleDOI
Clinical versus actuarial judgment
TL;DR: Research comparing these two approaches to decision-making shows the actuarial method to be superior, factors underlying the greater accuracy of actuarial methods, sources of resistance to the scientific findings, and the benefits of increased reliance on actuarial approaches are discussed.
Book
A Distribution-Free Theory of Nonparametric Regression
TL;DR: How to Construct Nonparametric Regression Estimates * Lower Bounds * Partitioning Estimates * Kernel Estimates * k-NN Estimates * Splitting the Sample * Cross Validation * Uniform Laws of Large Numbers