Modelling trends in digit preference patterns
In: Booth, J. G. (Ed.): Proceedings of the 24th International Workshop on Statistical Modelling, Ithaca 20 - 24 July, 2009, 81–88
Ithaca, NY, Cornell University, Department of Biological Statistics and Computational Biology (2009)
A two-dimensional generalization of a penalized composite link model is presented to model latent distributions with digit preference, where the strength of the misreporting pattern can vary over time. A general preference pattern is superimposed on a series of smooth latent densities, and this pattern is modulated for each measurement occasion. Smoothness of
the latent distributions is enforced by a difference penalty on neighbouring coefficients. An L1-ridge regression is used for the common misreporting pattern, and an additional weighted least-squares regression extracts the modulating vector. The BIC is used to optimize the smoothing parameters. We present a simulation study and an application for demonstrating the performance of our model and its practical characteristics.