In this study, a statistical cranium geometry model for 0-3 month old children was developed based on CT images using a combination of principal component analysis (PCA) and multivariate regression analysis. Radial basis function (RBF) was used to morph a baseline child head FE model into three pediatric head geometries, representing a newborn, a 1.5-month-old, and a 3-month-old infant head. These three models were used in a parametric study in nearvertex drop conditions to quantify the sensitivity of different material parameters. Finally, model validation was conducted against peak head accelerations in cadaver tests under different drop conditions, and optimization technique was used to determine the material properties. Results showed that the statistical cranium geometry model based on CT images included realistic cranium size and shape, suture size, and skull/suture thickness, for 0-3 month-old children. The three pediatric head models generated by morphing had a comparable mesh quality with the baseline model. The elastic modulus of skull had the dominant effect on most head impact response measurements. Head geometry was a significant factor affecting the maximal principal stress of the skull (P=0.002) and maximal principal strain of the suture (P=0.021) after controlling the skull material. In average the 3-month-old head predicted higher peak head acceleration (6.45% higher), maximal principal stress (64.75% higher) and strain (66.31% higher) of the suture, but lower maximal principal stress (25.71% lower) and strain (11.54% lower) of the skull than those from the newborn head. Material properties of the brain had little effects on head acceleration and strain/stress within the skull and suture. Elastic moduli of the skull, suture, dura, and scalp were well determined through optimization technique to match the cadaver test. The method developed in this study made it possible to investigate the age effects on geometry changes on pediatric head impact responses. The parametric study demonstrated that it is important to consider the material properties and geometric variations together when estimating pediatric head responses and predicting head injury risks.