Abstract

Binary or multinomial data often occur in agricultural and biological research. Advancements in measurement and video technologies now allow such data to be sequentially recorded through time or space. These data sets, however, can exhibit a serial correlation structure, which in turn, can bias and influence point estimates as well as inferences made regarding the data. Statistical methods using generalized mixed models and probability distributions such as the beta-binomial and correlated binomial have been proposed as potential solutions for estimating the parameters of interest in these cases. In this paper, we will explore the properties of these techniques through simulation studies and demonstrate each scenario using real data related to olfactometer choice tests of a seed eating weevil.

Keywords

Binary data, Serial Correlation, Bias Estimates, Inaccurate Inference

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Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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May 1st, 8:00 AM

ALTERNATIVE ESTIMATION TECHNIQUES FOR CORRELATED DISCRETE DATA

Binary or multinomial data often occur in agricultural and biological research. Advancements in measurement and video technologies now allow such data to be sequentially recorded through time or space. These data sets, however, can exhibit a serial correlation structure, which in turn, can bias and influence point estimates as well as inferences made regarding the data. Statistical methods using generalized mixed models and probability distributions such as the beta-binomial and correlated binomial have been proposed as potential solutions for estimating the parameters of interest in these cases. In this paper, we will explore the properties of these techniques through simulation studies and demonstrate each scenario using real data related to olfactometer choice tests of a seed eating weevil.