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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Automatic Intrusion Detection System Using Deep Recurrent Neural Network Paradigm
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Estimation and Updating Methods for Hedonic Valuation
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Advancing landscape sustainability science: theoretical foundation and synergies with innovations in methodology, design, and application
Chuan Liao,Jiangxiao Qiu,Bin Chen,Deliang Chen,Bojie Fu,Matei Georgescu,Chunyang He,G. Darrel Jenerette,Xia Li,Xiao-Yan Li,Xiao-Yan Li,Xin Li,Bading Qiuying,Peijun Shi,Peijun Shi,Jianguo Wu,Jianguo Wu +16 more
TL;DR: In this paper, the authors identify the major theoretical foundations of landscape sustainability science, discuss recent innovations in research methodology to advance LSS, summarize the extension of landscape design and geo-design, and examine the application of LSS for addressing sustainability challenges across multiple landscapes, highlighting that longterm regional sustainability can only be achieved by integrating context-based sustainability across agricultural, urban, and natural landscapes so as to minimize the regional ecological footprint and make advancement towards achieving the sustainable development goals.
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.
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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