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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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Published on: March 1, 2022

Sparse High Dimensional Models in Economics.

Jianqing Fan1, Jinchi Lv, Lei Qi

  • 1Bendheim Center for Finance, Princeton University, Princeton, New Jersey 08544.

Annual Review of Economics
|October 25, 2011
PubMed
Summary
This summary is machine-generated.

This review covers sparse high-dimensional models, focusing on penalized methods for variable selection in economics and finance. It details regularization techniques effective for handling extensive datasets and discusses their statistical properties.

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Last Updated: May 28, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Area of Science:

  • Statistics
  • Econometrics
  • Financial Modeling

Background:

  • High-dimensional data presents challenges for traditional statistical models.
  • Sparse models are crucial for analyzing datasets with many variables relative to observations.
  • Variable selection is key to identifying relevant predictors in complex models.

Purpose of the Study:

  • To review the literature on sparse high-dimensional models.
  • To highlight recent theoretical and methodological advancements.
  • To discuss applications in economics and finance.

Main Methods:

  • Focus on penalized least squares and penalized likelihood methods.
  • Emphasis on variable selection techniques within regularization frameworks.
  • Discussion of regularization limits and penalty function properties.

Main Results:

  • Penalized methods demonstrate effectiveness in high-dimensional sparse modeling.
  • Regularization techniques have defined limits regarding dimensionality.
  • Statistical properties of penalty functions are crucial for model performance.

Conclusions:

  • Sparse high-dimensional models are essential tools in modern data analysis.
  • Penalized methods offer robust variable selection capabilities.
  • Ongoing research addresses ultra-high dimensional modeling challenges.