In the analysis of fingertip photoplethysmograms (PPG), the Pulse Decomposition Analysis (PDA) has emerged as a powerful tool for the extraction of physiologically relevant information from the morphology of single digital volume pulse (DVP) cycles. In previously published works on the PDA, many different models are suggested. In this work, we conducted a data driven approach to address the question of which model to choose for the PDA. For this purpose, we compiled an extensive dataset of 7805 single DVP pulses that comprises most expectable pulse morphologies and conducted PDA simulations with four different basis functions types and a meaningful range of model orders. We then performed model selection based on the Corrected Akaike Information Criterion (AICc) with the aim of identifying the PDA models that provided the best fit. As a result, we found that a PDA model based on the linear superposition of three scaled Gamma basis functions was selected as the best fitting model in 28.1% of all pulses. The second highest relative selection frequency of 14.4% was achieved by fitting two Rayleigh functions. Consequently, we recommend to consider the employment of this PDA model in further work on the PDA.