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BRIEF RESEARCH REPORT article

Front. Appl. Math. Stat.
Sec. Mathematical Biology
Volume 10 - 2024 | doi: 10.3389/fams.2024.1374832

Determination of Sample Size for a Multinomial Model Coupled with the Phenology Model

Provisionally accepted
Martyna Lukaszewicz Martyna Lukaszewicz 1*Brian Dennis Brian Dennis 2,3
  • 1 Bioinformatics and Computational Biology Program, University of Idaho, Moscow, United States
  • 2 Department of Fish and Wildlife Sciences, College of Natural Resources, University of Idaho, Moscow, Idaho, United States
  • 3 Department of Mathematics and Statistical Science, University of Idaho, Moscow, Idaho, United States

The final, formatted version of the article will be published soon.

    Predicting the timing of phenological events is important in agriculture, especially high revenue products. A project sponsored by USDA-ARS had the objective of adapting a previously developed model for estimating proportions of insects in different development stages as a function of temperature (degree) and time (days) for predicting bloom in almond orchards. Data for the model normally form a two-way table of counts, with rows corresponding to sample percentages of different development stages, and columns to sampling times. In this paper, we report a technique developed to estimate sample sizes of multinomial and product multinomial models using a method of moments and determine the empirical coverage of sample size. This paper aims to determine an appropriate sample size for data collection. This involves establishing a sampling distribution for the Pearson statistic, defined as the product of the sample size and the deviance of empirical proportions from population proportions. The intended outcome is to predict the optimal timing for harvesting crops at desired development stages when coupled with 1 the phenology model, for which variability of the maximum likelihood estimates of the phenology model depends on sample size.

    Keywords: Chi-squared, Maximum likelihood parameter estimation, Method of moments, Missing counts, pooling, sparse datasets

    Received: 22 Jan 2024; Accepted: 17 Jun 2024.

    Copyright: © 2024 Lukaszewicz and Dennis. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Martyna Lukaszewicz, Bioinformatics and Computational Biology Program, University of Idaho, Moscow, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.