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Related Experiment Video

Updated: Jul 7, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Marked regularity models.

R M Cramblitt1, M R Bell

  • 1SVS RandD Syst. Inc., Albuquerque, NM. rcramblitt@svsinc.com

IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
|February 2, 2008
PubMed
Summary
This summary is machine-generated.

This study generalizes the regularity model for event occurrence, allowing event amplitudes to vary. Accurate parameter estimation requires considering mark variability, especially when signal-to-noise ratio is low.

Related Experiment Videos

Last Updated: Jul 7, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Area of Science:

  • Signal Processing
  • Statistical Modeling
  • Point Processes

Background:

  • The regularity model describes event occurrence frequency and regularity.
  • Generalization allows for random process sampling of event amplitudes.
  • Accurate parameter estimation is crucial for understanding complex data.

Purpose of the Study:

  • To generalize the stationary point process regularity model.
  • To investigate parameter estimation accuracy for regularity and mark processes.
  • To assess the impact of mark variability and measurement noise on estimation.

Main Methods:

  • Developed closed-form approximations for power spectra of data segments.
  • Estimated regularity and mark process parameters by minimizing spectral error.
  • Analyzed parameter estimation accuracy under varying noise and mark properties.

Main Results:

  • Joint estimation accuracy is limited by low signal-to-noise ratio (SMNPR).
  • Marginal estimation accuracy depends on mark process consideration, noise, and SMNPR.
  • Ignoring mark variability degrades accuracy, particularly for small SMNPR.

Conclusions:

  • Accurate estimation in generalized regularity models necessitates accounting for mark process characteristics.
  • The ratio of the square of the mean of the marks to the variance of the marks (SMNPR) is a critical factor.
  • Findings are illustrated using acoustic scattering and simulated ultrasound tissue measurements.