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Updated: Jun 22, 2026

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

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Published on: July 3, 2020

Model-based multiplicity estimation of population size.

Eugene M Laska¹, Morris Meisner, Joseph Wanderling

¹Statistics and Services Research Division, The Nathan S. Kline Institute for Psychiatric Research, New York University School of Medicine, Orangeburg, NY 10962, USA. laska@nki.rfmh.org

Statistics in Medicine

|July 3, 2009

Summary

This summary is machine-generated.

This study introduces a new survey method for estimating population size (N) using purposively selected samples. This model-based approach can yield more precise estimates than traditional random sampling when the sample is typical.

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Area of Science:

Statistics
Survey Methodology
Epidemiology

Background:

Estimating population size (N) is crucial in various fields.
Traditional methods rely on probability sampling, which can be resource-intensive.
Purposive sampling offers an alternative for selecting 'typical' samples.

Purpose of the Study:

To develop and evaluate a model-based estimator for population size (N).
To compare the efficiency of purposive sampling with traditional random sampling.
To investigate the conditions under which model-based inference is valid.

Main Methods:

A novel estimator using participant data (u, j) on selection units (w of K).
Purposive selection of survey units to maximize sample typicality.
Model-based analysis for statistical inference.

Main Results:

Maximum Likelihood (ML) estimators for N and E(J) are unbiased for typical samples.
Model-based variance can be smaller than random sampling variance under specific conditions.
The study highlights the importance of sample selection for model validity.

Conclusions:

Purposive sampling with a model-based approach offers a potentially more efficient method for estimating population size (N).
Careful selection of 'typical' samples is critical for the validity and precision of the estimator.
Further research is needed to develop methods for assessing sample typicality.