scholarly journals A Data-Driven Approach for Multiscale Elliptic PDEs with Random Coefficients Based on Intrinsic Dimension Reduction

2020 ◽  
Vol 18 (3) ◽  
pp. 1242-1271
Author(s):  
Sijing Li ◽  
Zhiwen Zhang ◽  
Hongkai Zhao
Keyword(s):  
Dimension Reduction ◽  
Random Coefficients ◽  
Data Driven ◽  
Elliptic Pdes ◽  
Intrinsic Dimension ◽  
Data Driven Approach

A Data-Driven Stochastic Method for Elliptic PDEs with Random Coefficients

2013 ◽  
Vol 1 (1) ◽  
pp. 452-493 ◽  
Author(s):  
Mulin Cheng ◽  
Thomas Y. Hou ◽  
Mike Yan ◽  
Zhiwen Zhang
Keyword(s):  
Random Coefficients ◽  
Stochastic Method ◽  
Data Driven ◽  
Elliptic Pdes

scholarly journals A Multiscale Data-Driven Stochastic Method for Elliptic PDEs with Random Coefficients

2015 ◽  
Vol 13 (1) ◽  
pp. 173-204 ◽  
Author(s):  
Zhiwen Zhang ◽  
Maolin Ci ◽  
Thomas Y. Hou
Keyword(s):  
Random Coefficients ◽  
Stochastic Method ◽  
Data Driven ◽  
Elliptic Pdes

scholarly journals BLP-2LASSO for aggregate discrete choice models with rich covariates

2019 ◽  
Vol 22 (3) ◽  
pp. 262-281 ◽  
Author(s):  
Benjamin J Gillen ◽  
Sergio Montero ◽  
Hyungsik Roger Moon ◽  
Matthew Shum
Keyword(s):  
Ad Hoc ◽  
Demographic Data ◽  
Discrete Choice Models ◽  
Random Coefficients ◽  
Data Driven ◽  
Preference Heterogeneity ◽  
High Dimensional ◽  
Differentiated Products ◽  
Estimation Algorithms ◽  
Data Driven Approach

Summary We introduce the BLP-2LASSO model, which augments the classic BLP (Berry, Levinsohn, and Pakes, 1995) random-coefficients logit model to allow for data-driven selection among a high-dimensional set of control variables using the 'double-LASSO' procedure proposed by Belloni, Chernozhukov, and Hansen (2013). Economists often study consumers’ aggregate behaviour across markets choosing from a menu of differentiated products. In this analysis, local demographic characteristics can serve as controls for market-specific preference heterogeneity. Given rich demographic data, implementing these models requires specifying which variables to include in the analysis, an ad hoc process typically guided primarily by a researcher’s intuition. We propose a data-driven approach to estimate these models, applying penalized estimation algorithms from the recent literature in high-dimensional econometrics. Our application explores the effect of campaign spending on vote shares in data from Mexican elections.

Extended-Range Prediction with Low-Dimensional, Stochastic-Dynamic Models: A Data-driven Approach

2012 ◽  
Author(s):  
Michael Ghil ◽  
Mickael D. Chekroun ◽  
Dmitri Kondrashov ◽  
Michael K. Tippett ◽  
Andrew Robertson ◽  
...  
Keyword(s):  
Dynamic Models ◽  
Data Driven ◽  
Stochastic Dynamic ◽  
Extended Range ◽  
Data Driven Approach ◽  
Low Dimensional

A Mostly Data-Driven Approach to Inverse Text Normalization

2017 ◽  
Author(s):  
Ernest Pusateri ◽  
Bharat Ram Ambati ◽  
Elizabeth Brooks ◽  
Ondrej Platek ◽  
Donald McAllaster ◽  
...  
Keyword(s):  
Data Driven ◽  
Data Driven Approach ◽  
Text Normalization
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