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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

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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

Maximum likelihood estimation for semiparametric density ratio model.

Guoqing Diao1, Jing Ning, Jing Qin

  • 1George Mason University, USA.

The International Journal of Biostatistics
|June 30, 2012
PubMed
Summary
This summary is machine-generated.

We introduce a new nonparametric likelihood method for density ratio models. This approach efficiently estimates regression parameters and baseline density simultaneously, offering greater versatility than existing restrictive methods.

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Area of Science:

  • Statistics
  • Econometrics

Background:

  • The semiparametric density ratio model is crucial for studying regression effects.
  • Existing methods often rely on restrictive multi-sample data or conditional likelihood functions, limiting baseline density estimation.

Purpose of the Study:

  • To propose efficient estimation procedures for the density ratio model using nonparametric likelihood.
  • To overcome the limitations of existing restrictive methods and enable simultaneous estimation of regression parameters and baseline density.

Main Methods:

  • Developed nonparametric likelihood estimation procedures.
  • The approach accommodates general covariate forms.
  • Simultaneous estimation of regression parameters and baseline density is achieved.

Main Results:

  • The proposed nonparametric maximum likelihood estimators are consistent, asymptotically normal, and asymptotically efficient.
  • Simulation studies confirm the practical effectiveness of the new methods.

Conclusions:

  • The nonparametric likelihood approach offers a more versatile alternative to conditional likelihood methods for density ratio models.
  • This method is particularly advantageous for estimating conditional means and other outcome quantities.