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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A picture is worth a thousand words: Measuring investor sentiment by combining machine learning and photos from news
TL;DR: In this article , a daily market-level investor sentiment index (Photo Pessimism) was introduced from a large sample of news photos, which predicts market return reversals and trading volume.
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Prediction of problematic social media use (PSU) using machine learning approaches
TL;DR: In this article, problematic social media use (PSU) was modeled using machine learning with artificial neural networks (ANN) and support vector machines (SVM), and fifteen predictor variables were examined in predicting PSU, including social media usage habits (frequency of daily social media using, history of socialmedia usage, frequency of checking social media accounts, number of shares on social media, and number of accounts), desire for being liked, envy of the life of others, narcissistic personality traits (exhibitionism, grandiose fantasies, manipulativeness, thrill-seeking, narcissistic admiration
ReportDOI
Computer Vision and Real Estate: Do Looks Matter and Do Incentives Determine Looks
TL;DR: This article found that one standard deviation improvement in the appearance of a home in Boston is associated with a.16 log point increase in the home's value, or about $68,000 at the sample mean.
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Le moment big data des sciences sociales
Gilles Bastin,Paola Tubaro +1 more
TL;DR: In this article, Gilles Bastin et Paola Tubaro introduce a special edition of the Revue Francaise de Sociologie devoted to big data, societes et sciences sociales.
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AI in actuarial science – a review of recent advances – part 1
TL;DR: How actuarial science may adapt and evolve in the coming years to incorporate these new techniques and methodologies based on a modern approach to designing, fitting and applying neural networks, generally referred to as “Deep Learning.”
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.
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