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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Published on: July 3, 2020

Parametrically Guided Generalized Additive Models with Application to Mergers and Acquisitions Data.

Jianqing Fan1, Arnab Maity, Yihui Wang

  • 1Frederick L. Moore '18 Professor of Finance, Department of Operations Research and Financial Engineering, Princeton University, Princeton, NJ 08544, USA.

Journal of Nonparametric Statistics
|May 7, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for generalized nonparametric additive models, using prior information as a parametric guide. This approach can significantly reduce estimation variance when prior information is accurate.

Keywords:
generalized additive modelleveraged buyoutlocal polynomialmergers and acquisitionsparametric guide

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Basics of Multivariate Analysis in Neuroimaging Data
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Area of Science:

  • Statistics
  • Econometrics

Background:

  • Generalized nonparametric additive models (GNAMs) offer flexibility in modeling covariate effects.
  • Prior information on regression function shapes is often available but underutilized.

Purpose of the Study:

  • To propose an estimation procedure for GNAMs that incorporates prior information as a parametric guide.
  • To enhance the efficiency of GNAMs by leveraging existing knowledge.

Main Methods:

  • Posit parametric families for regression functions based on prior information (parametric guides).
  • Estimate residual nonparametric functions using GNAMs after removing parametric trends.
  • Combine parametric and nonparametric components for final estimates.

Main Results:

  • The proposed method's asymptotic properties are investigated.
  • Significant reduction in asymptotic variance is achievable with appropriate parametric guides.
  • The method maintains the same asymptotic variance as unguided estimators when guides are suboptimal.

Conclusions:

  • Incorporating prior information via parametric guides offers a statistically efficient approach to GNAMs.
  • The method is validated through simulation studies and application to real-world data (mergers and acquisitions).