Advances in sensor technology necessitate fast and accurate methods to deal with an ever growing wellspring of information. Anomaly detection algorithms for hyperspectral imagery (HSI) are an important first step in the analysis chain which can reduce the overall amount of data to be processed. The actual amount of data reduced depends greatly on the accuracy of the anomaly detection algorithm implemented. Most, if not all, anomaly detection algorithms require a user to identify initial parameters. These parameters, or controls, affect overall algorithm performance. Regardless of the anomaly detector being utilized, algorithm performance is often negatively impacted by uncontrollable noise factors which introduce additional variance into the process. In the case of HSI, the noise variables are embedded in the image under consideration. Robust parameter design (RPD) offers a method to model the controls as well as the noise variables and identify robust parameters. This research identifies image noise characteristics necessary to perform RPD on HSI. Additionally, a new data splitting algorithm to predict classifier performance with sparse data sets is presented. Finally, the standard RPD model is extended to consider higher order noise coefficients. Mean and variance RPD models are optimized in a dual response function. Results are presented from simulations as well as applications involving two anomaly detection algorithms, the Reed-Xiaoli anomaly detector and the autonomous global anomaly detector.