Examining Multiple Imputation for Measurement Error Correction in Count Data with Excess Zeros

Measurement error and missing data are two common problems in wildlife population surveys. These data are collected from the environment and may be missing or measured with error when the observer’s ability to see the animal is obscured. Methods such as video transects for estimating red snapper abundance and aerial surveys for estimating moose population sizes are highly affected by these problems since total abundance will be underestimated if missing/mismeasured counts are ignored. We shall refer to this problem as visibility bias; it occurs when the true counts are observed when visibility is high, partially observed when visibility is low (mismeasured), and unobservable when visibility is lost (missing). In addition, data from animal population surveys are often sparse since not all sampled regions are inhabited by the species. In this dissertation, we examine several multiple imputation techniques which can be used to correct measurement error in sparse count data that are subject to visibility bias. We present several off-the-shelf imputation models and our developed imputation model HBZIP as well as a modified hot deck imputation approach. We evaluate their performance for estimating total abundance and habitat occupancy rate and assess the robustness of the HBZIP model against visibility model misspecification. We further incorporate Bayesian model averaging approach into the HBZIP model to mitigate the impact of visibility model uncertainty and illustrate its application on real data collected from moose population surveys