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Letter to the editor: Fitting truncated normal distributions.

Iain L MacDonald1

  • 1Actuarial Science, University of Cape Town, Rondebosch, South Africa.

Statistical Methods in Medical Research
|May 30, 2017
PubMed
Summary
This summary is machine-generated.

Direct numerical maximization is a straightforward method for fitting truncated normal distributions, challenging previous assertions about the EM algorithm

Keywords:
EM algorithmTruncated normal distributionlikelihoodlognormal distributionnumerical optimization

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Area of Science:

  • Statistics
  • Computational Statistics

Background:

  • The EM algorithm is a common method for parameter estimation in statistical models.
  • Fitting truncated normal distributions presents unique challenges in statistical modeling.

Discussion:

  • This commentary addresses the fitting of truncated normal distributions using the EM algorithm.
  • It highlights the feasibility of direct numerical maximization of the likelihood function for this task.

Key Insights:

  • Direct numerical maximization offers a simpler alternative to the EM algorithm for fitting truncated normal distributions.
  • The authors' assertion regarding the complexity of direct maximization is contested.

Outlook:

  • Further exploration of direct maximization methods for complex distributions is warranted.
  • This could lead to more efficient statistical inference techniques.