S-Plus/R Source Code for individual functions:

Data sets


[1] Local Principal Curves (Einbeck, Tutz & Evers, 2005). Statistics and Computing 15, 301-313.
[2] Exploring Multivariate Data Structures with Local Principal Curves (Einbeck, Tutz & Evers, 2005). Proceedings of the GfKl 2004.
[3] Data compression and regression based on local principal curves (Einbeck, Evers & Hinchliff, 2010). Proceedings of the GfKl 2008.
[4] Data compression and regression through local principal curves and surfaces (Einbeck, Evers, & Powell, 2010). International Journal of Neural Systems 20, 177–192.
[5] A General Maximum Likelihood Analysis of Overdispersion in Generalized Linear Models (Aitkin, 1996). Statistics and Computing 6 , 251-262.
[6] A note on NPML estimation for exponential family regression models with unspecified dispersion parameter (Einbeck & Hinde, 2006). Austrian Journal of Statistics 35 , 233-243, 2006.
[7] Modelling beyond Regression Functions: an Application of Multimodal Regression to Speed-Flow Data (Einbeck & Tutz, 2006). Journal of the Royal Statistical Society, Series C (Applied Statistics) 55, 461-475.
[8] Weighted Repeated Median Smoothing and Filtering (Fried, Einbeck & Gather, 2007). JASA , 102, 1300--1308.
[9] Online Monitoring with Local Smoothing Methods and Adaptive Ridging (Einbeck & Kauermann, 2003). Journal of Statistical Computation and Simulation 73, 913-929.
[10] Using principal curves to analyze traffic patterns on freeways (Einbeck & Dwyer, 2011). Transportmetrica, 7, 229-246.
[11] Implementation of a local principal curves alghorithm for neutrino interaction reconstruction in a liquid argon volume (Back et al, 2014). European Physical Journal C 74 pp. 2832
[12] A study of online and blockwise updating of the EM algorithm for Gaussian mixtures (Einbeck and Bonetti, 2014): In: T. Kneib et al (Eds.). Proceedings of the 29th International Workshop on Statistical Modelling. Göttingen, Germany, 14-18 July 2014, pages 35-38.
[13] k-Boxplots for Mixture Data (Qarmalah, Einbeck, and Coolen, 2016): Statistical Papers, doi 10.1007/s00362-016-0774-7.
[14] A statistical framework for radiation dose estimation with uncertainty quantification from the γ-H2AX assay (Einbeck et al, 2018). PLOS ONE 13(11): e0207464.

Jochen Einbeck