Model-Based clustering for cross-sectional time series data

Abstract

A model-based clustering method for cross-sectional time series data is proposed and applied to crop insurance programs. To design an effective grouprisk plan, an important step is to group together the farms that resemble each other and decide the number of clusters, both of which can be achieved via the model-based clustering. The mixture maximum likelihood is employed for inferences. However, with the presence of correlation and missing values, the exact maximum likelihood estimators (MLEs) are difficult to obtain. An approach for obtaining approximate MLEs is proposed and evaluated through simulation studies. A bootstrapping method is used to choose the number of components in the mixture model.