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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Toward generalizing the use of artificial intelligence in nephrology and kidney transplantation
TL;DR: In this article , the authors provide an overview of AI from a medical perspective and discuss the technical challenges of using AI in the kidney transplantation (KT) field, focusing on the unmet needs and the potential role that AI can play to fill these gaps.
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Do Raw Accounting Data Convey Information About R&D Costs Accounting Treatment? An Investigation With Machine Learning
Personalized E-Learning on Social Web with Machine Learning
TL;DR: A new technology for classifying the users engaged in facebook based on Particle Swarm Optimization (PSO) is proposed and shows that the performance of content-based engaged users is more reliable semantically for applied on personalized social web.
Capitalization versus expensing of R & D costs under IAS 38 : An empirical investigation using machine learning
TL;DR: In this article , the authors study whether financial ratios or raw accounting data can achieve better out of sample predictions of R&D accounting treatment, and they show that when the traditional logistic regression is used, financial ratios outperform raw data.
References
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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.
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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