Jochen Einbeck - Publications

Journal Articles

Almohaimeed, Amani & Einbeck, Jochen (2023). A Sequential Cross-Sectional Analysis Producing Robust Weekly COVID-19 Rates for South East Asian Countries. Viruses 15, 1572, doi:10.3390/v15071572
Bar-Lev, Shaul K., Batsidis, Apostolos, Einbeck, Jochen, Liu, Xu & Ren, Panpan (2023). Cumulant-Based Goodness-of-Fit Tests for the Tweedie, Bar-Lev and Enis Class of Distributions. Mathematics, 11(7), 1603, doi 10.3390/math11071603.
Hernández, Alfredo, Endesfelder, Biodose Tools: an R shiny application for biological dosimetry. International Journal of Radiation Biology, doi: 10.1080/09553002.2023.2176564
Basu, T., Troffaes, M. C. M. & Einbeck, J. (2023). A robust Bayesian analysis of variable selection under prior ignorance. Sankhya A 85, 1014-1057.[ArXiv version].
Almohaimeed, A., Einbeck, J., Qarmalah, N., and Alkhidhr, H. (2022). Using Random Effect Models to Produce Robust Estimates of Death Rates in COVID-19 Data. International Journal of Environmental Research and Public Health 19(22), 14960; doi 10.3390/ijerph192214960
Tolley, C. L., Watson, N. W., Heed, A., Einbeck, J., Medows, S., Wood, L., Campbell, L. & Slight, S.P. (2022). The impact of a novel medication scanner on administration errors in the hospital setting: a before and after feasibility study. BMC Medical Informatics and Decision Making 22 (1), 86.
Almohaimeed, A. & Einbeck, J. (2022). Response transformations for random effect and variance component models. Statistical Modelling, 22 (4), 297-326, doi 10.1177/1471082X20966919.
Singh, A., Uwimpuhwe, G., Li, M., Einbeck, J., Higgins, S. & Kasim, A. (2022). Multisite educational trials: estimating the effect size and its confidence intervals. International Journal of Research & Method in Education 45 (1),18-38, doi 10.1080/1743727X.2021.1882416.
Errington, A., Einbeck, J., Cumming, J., Rössler, U. & Endesfelder, D. (2022). The effect of data aggregation on dispersion estimates in count data models. The International Journal of Biostatistics 18(1), pp. 183-202, doi 10.1515/ijb-2020-0079.
Reissland, N., Einbeck, J., Wood, R. & Lane, A. (2021). Effects of maternal mental health on prenatal movement profiles in twins and singletons. Acta paediatrica 110(9), 2553-2558.
Wilson, P. & Einbeck, J. (2021). A graphical tool for assessing the suitability of a count regression model. Austrian Journal of Statistics, 50(1), 1-23.
Reissland, N., Wood, R., Einbeck, J. & Lane, A. (2020). Effects of maternal mental health on fetal visual preference for face-like compared to non-face like light stimulation. Early Human Development, 151, 105227.
Endesfelder, David, Kulka, Ulrike, Einbeck, Jochen & Oestreicher, Ursula (2020). Improving the accuracy of dose estimates from automatically scored dicentric chromosomes by accounting for chromosome number. International Journal of Radiation Biology, 96(12), 1571-1584.
Reissland, N., Millard, A., Wood, R., Ustun, B., McFaul, C., Froggatt, S. & Einbeck, J. (2020). Prenatal effects of maternal nutritional stress and mental health on the fetal movement profile. Archives of Obstetrics and Gynaecology 302(1): 65-75.
Einbeck, J., Kalantan, Z. & Kruger, U. (2020). Practical Considerations on Nonparametric Methods for Estimating Intrinsic Dimensions of Nonlinear Data Structures. International Journal of Pattern Recognition and Artificial Intelligence, 2058010.
Kalantan, Z., and Einbeck, J. (2019). Quantile-Based Estimation of the Finite Cauchy Mixture Model. Symmetry 11(9): 1186.
Wilson, P. and Einbeck, J. (2019). A new and intuitive test for zero modification. Statistical Modelling, 19(4), 341-361. doi 1471082X1876227.
Einbeck, J., Ainsbury, E. A., Sales, R., Barnard, S., Kaestle, F. & Higueras, M. (2018). A statistical framework for radiation dose estimation with uncertainty quantification from the γ-H2AX assay. PLOS ONE 13(11): e0207464. [App].
Marques da Silva Júnior, A.H., Einbeck, J. and Craig, Peter S. (2018). Fisher information under Gaussian quadrature models. Statistica Neerlandica 72, 74-89.
Qarmalah, N., Einbeck, J. and Coolen, F. (2018). k-Boxplots for mixture data. Statistical Papers 59, 513-528, .doi 10.1007/s00362-016-0774-7. [Implementation in R package UEM.]
Einbeck, J., and Meintanis, S. (2017). Self consistency–based–tests for bivariate distributions. Journal of Statistical Theory and Practice 11, 478-492 [Accepted version].
Qarmalah, N.M., Einbeck, J. and Coolen, F.P.A. (2017). Mixture Models for Prediction from Time Series, with Application to Energy Use Data. Archives of Data Science Series A 2, doi:10.5445/KSP/1000058749/07.
Ainsbury, E.A., Higueras, M., Puig, P., Einbeck, J., Samaga, D., Barquinero, J.F., Barrios, L., Brzozowska, B., Fattibene, P., Gregoire, E., Jaworska, A., Lloyd, D., Oestreicher, U., Romm, H., Rothkamm, K., Roy, L., Sommer, S., Terzoudi, G., Thierens, H., Trompier, F., Vral, A., and Woda, C. (2017). Uncertainty of fast biological radiation dose assessment for emergency response scenarios, International Journal of Radiation Biology, 93(1): 127-135, doi 10.1080/09553002.2016.1227106.
Jackson, S.E., Einbeck, J., Kasim, A. and Talloen, W. (2016), The correlation threshold as a strategy for gene filtering, with application to irritable bowel syndrome and breast cancer microarray data, Reinvention 9(2).
Oliveira, M., Einbeck, J., Higueras, M., Ainsbury, E.A., Puig, P. and Rothkamm, K. (2016). Zero-inflated regression models for radiation-induced chromosome aberration data: A comparative study, Biometrical Journal 58, 259–279.
Einbeck, J., Jackson, S., and Kasim, A. (2015). A summer with genes: Simple disease classification from microarray data. Mathematics Today 51 (4), 186-188.
Meintanis, S., and Einbeck, J. (2015). Validation tests for semi-parametric models, Journal of Statistical Computation and Simulation 85, 131-146, full text on DRO
Back, J.J., Barker, G.J., Boyd, S.B., Einbeck, J., Haigh, M., Morgan, B., Oakley, B., Ramachers, Y.A., Roythorne, D. (2014). Implementation of a local principal curves algorithm for neutrino interaction reconstruction in a liquid argon volume, The European Physical Journal C 74:2832 [full text on arxiv].
Einbeck, J., and Zayed, M. (2014). Some asymptotics for localized principal components and curves, Communications in Statistics - Theory and Methods 43, 1736-1749, full text on DRO
Taylor, J., and Einbeck, J. (2013). Challenging the curse of dimensionality in multivariate local linear regression, Computational Statistics 28, 955-976, full text on DRO
Einbeck, J., and Taylor, J. (2013). A number-of-modes reference rule for density estimation under multimodality, Statistica Neerlandica 67, 54-66, doi:10.1111/j.1467-9574.2012.00531.x, full text on DRO
Einbeck, J. and Kalantan, Z. (2013). Intrinsic Dimensionality Estimation for High-dimensional Data Sets: New Approaches for the Computation of Correlation Dimension. Journal of Emerging Technologies in Web Intelligence 5, 91-97.
Meintanis, S.G., and Einbeck, J. (2012). Goodness-of-fit tests in semi-linear models, Statistics and Computing 22, 967-979, full text on DRO
Einbeck, J. (2011). Bandwidth selection for mean-shift based unsupervised learning techniques - a unified approach via self-coverage, Journal of Pattern Recognition Research 6 , 175-192.
Einbeck, J. and Dwyer, J. (2011): Using principal curves to analyse traffic patterns on freeways, Transportmetrica 7, 229--246, full text on DRO
Einbeck, J., Evers, L., & Powell, B. (2010). Data compression and regression through local principal curves and surfaces, International Journal of Neural Systems 20, 177–192. [full text on DRO; Software and data sets available in R package LPCM .]
Einbeck, J., and Augustin, T. (2009): On design-weighted local fitting and its relation to the Horvitz-Thompson Estimator. Statistica Sinica 19 , 103--123.
Fried, R., Einbeck, J., & Gather, U. (2007). Weighted Repeated Median Smoothing and Filtering. Journal of the American Statistical Association 102, 1300--1308, full text on DRO
Newell, J., Higgins, D., Madden, N., Cruickshank, J., Einbeck, J., McMillan, K., McDonald, R. (2007): Software for calculating blood lactate endurance markers. Journal of Sports Science 25, 1403--1409.
Einbeck, J., Hinde, J., & Darnell, R. (2007). A new package for fitting random effect models: The npmlreg package. R News 7, 26--30.
Einbeck, J., & Tutz, G. (2006): Modelling beyond regression functions: An application of multimodal regression to speed-flow data, Journal of the Royal Statistical Society, Series C (Applied Statistics) 55, 461-475.
Einbeck, J., & Hinde J. (2006). A note on NPML estimation for exponential family regression models with unspecified dispersion parameter, Austrian Journal of Statistics 35, 233-243, 2006.
Einbeck, J., Tutz, G., & Evers, L. (2005). Local Principal Curves, Statistics and Computing 15, 301-313.
Einbeck, J. (2004). Local Fitting with a Power Basis, REVSTAT - Statistical Journal 2, 101-126.
Einbeck, J. (2004). A Simple Unifying Formula for Taylor's Theorem and Cauchy's Mean Value Theorem, International Journal of Pure and Applied Mathematics 14, 69-74.
Einbeck, J., André C.D.S. & Singer, J. M. (2004): Local Smoothing with Robustness against Outlying Predictors , Environmetrics 15, 541-554.
Einbeck, J. (2003). Multivariate Local Fitting with General Basis Functions, Computational Statistics 18, 185--203.
Einbeck, J. & Kauermann, G. (2003). Online Monitoring with Local Smoothing Methods and Adaptive Ridging, Journal of Statistical Computation and Simulation 73, 913-929.

Discussions

Einbeck, J. (2013): Discussion of `Beyond mean regression' (by Th. Kneib), Statistical Modelling 13, 349-354.

Conference Proceedings

Zhang, Y. & Einbeck, J. (2022). Simultaneous linear dimension reduction and clustering with flexible variance matrices. in Torelli, N., Bellio, R., and Muggeo, V. (Eds.), Proceedings of the 36th International Workshop of Statistical Modelling, Trieste, Italy, 18-22 July 2022, pages 612-617.
Basu, T., Einbeck, J. & Troffaes, M. (2020), A sensitivity analysis and error bounds for the adaptive lasso, in Irigoien, I., Lee, D.-J., Martinez-Minaya, J. & Rodriguez-Alvarez, M.X. (Eds.), Proceedings of the 35th International Workshop on Statistical Modelling, Bilbao, Universidad del Pais Vasco, 278-281.
Basu, Tathagata, Troffaes, Matthias C. M. & Einbeck, Jochen (2020), Binary Credal Classification Under Sparsity Constraints, in Lesot, Marie-Jeanne, Vieira, Susana, Reformat, Marek Z., Carvalho, Joao Paulo, Wilbik, Anna, Bouchon-Meunier, Bernadette & Yager, Ronald R. eds, Information Processing and Management of Uncertainty in Knowledge-Based Systems. Lisbon, Springer, 82-95.
Almohaimeed, A., and Einbeck, J. (2018). Box-Cox response transformations for random effect models. Proceedings of the 33rd International Workshop on Statistical Modelling, Bristol, UK, 15-20/07/2018, Vol. 2, 1-6.
Wilson, P. and Einbeck, J. (2017). Sample quantiles corresponding to mid p-values for zero-modification tests (2017). In: Grzegorczyk, M. and Ceoldo, G. (2017). Proceedings of the 32nd International Workshop on Statistical Modelling, Groningen, Netherlands, 3-7 July 2017, 275-279.
Einbeck, J., Gray, E., Sofroniou, N., Marques da Silva-Junior, A.H., Gledhill, J. (2017). Confidence intervals for posterior intercepts, with application to the PIAAC literacy survey. In: Grzegorczyk, M. and Ceoldo, G. (2017). Proceedings of the 32nd International Workshop on Statistical Modelling, Groningen, Netherlands, 3-7 July 2017, 217-222.
da Silva-Junior, A.H.M., Einbeck, J., and Craig, P.S. (2016). Gradient test for generalised linear models with random effects. In: Dupuys, J.-F., and Josse, J. (Eds). Proceedings of the 31st International Workshop on Statistical Modelling , Rennes, France, 4-8 July 2016, pages 213-218.
Einbeck, J., and Wilson, P. (2016). A diagnostic plot for assessing model fit in count data models. In: Dupuys, J.-F., and Josse, J. (Eds). Proceedings of the 31st International Workshop on Statistical Modelling, Rennes, France, 4-8 July 2016, pages 103-108.
Wilson, P. and Einbeck, J. (2016). On statistical testing and mean parameter estimation for zero-modification in count data regression. In: Dupuys, J.-F., and Josse, J. (Eds). Proceedings of the 31st International Workshop on Statistical Modelling, Rennes, France, 4-8 July 2016, pages 325-330.
Wilson, P. and Einbeck, J. (2015). A simple and intuitive test for number-inflation or number-deflation. In: Wagner, H. and Friedl, H. (Eds). Proceedings of the 30th International Workshop on Statistical Modelling, Linz, Austria, 6-10 July 2015, Vol 2, pages 299-302.
Bonetti, D., Delbem, A., and Einbeck, J. (2014). Bivariate Estimation of Distribution Algorithms for Protein Structure Prediction. In: T. Kneib et al (Eds.). Proceedings of the 29th International Workshop on Statistical Modelling. Göttingen, Germany, 14-18 July 2014, pages 15-18.
Einbeck, J., and Bonetti, D. (2014). A study of online and blockwise updating of the EM algorithm for Gaussian mixtures, In: T. Kneib et al (Eds.). Proceedings of the 29th International Workshop on Statistical Modelling. Göttingen, Germany, 14-18 July 2014, pages 35-38.
Tsiftsi, T., Jermyn1, I., and Einbeck, J.(2014): Bayesian shape modelling of cross-sectional geological data. In: T. Kneib et al. (Eds.). Proceedings of the 29th International Workshop on Statistical Modelling. Göttingen, Germany, 14-18 July 2014, pages 161-164.
Kalantan, Z. and Einbeck, J. (2012). On the computation of the correlation integral for fractal dimension estimation , International Conference on Statistics in Science, Business, and Engineering (ICSSBE), IEEE conference publications, doi 10.1109/ICSSBE.2012.6396531, pages 80-85.
Kalantan, Z., and Einbeck, J. (2012): An overview of intrinsic dimension estimation techniques. Proceedings of the 1st ISM International Statistical Conference 2012 , 516 - 524, 4-6 September 2012, PERSADA Johor Bahru, Johor, Malaysia.
Einbeck, J., Isaac, B., Evers, L., and Parente, A. (2012): Penalized regression on principal manifolds with application to combustion modelling, In: Komárek, A., and Nagy, S. (Eds): Proceedings of the 27th International Workshop on Statistical Modelling, Prague, pages 117-122.
Lawson, A., and Einbeck, J. (2012): Generative Linear Mixture Modelling, In: Komárek, A., and Nagy, S. (Eds): Proceedings of the 27th International Workshop on Statistical Modelling, Prague, pages 595-600.
Taylor, J., and Einbeck, J. (2011): Multivariate regression smoothing through the 'fallling net' . In: Conesa et al. (Eds.): 26th International Workshop on Statistical Modelling, Valencia, 11-15 July 2011, pages 597-602.
Einbeck, J. & Evers, L. (2010): Localized regression on principal manifolds . In A. Bowman (Ed.): 25th International Workshop on Statistical Modelling, University of Glasgow, 5-9th July, 2010, Proceedings, pages 179-184.
Zayed, M., and Einbeck, J. (2010): Constructing Economic Summary Indexes via Principal Curves, COMPSTAT 2010 Proceedings (e-book), pages 1709-1716, ISBN 978-3-7908-2603-6.
Taylor, J. & Einbeck, J. (2010): Strategies for local smoothing in high dimensions: Using density thresholds and adapted GCV. In A. Bowman (Ed.) : 25th International Workshop on Statistical Modelling, University of Glasgow 5-9th July, 2010, Proceedings, pages 525-528. [ Equation (5) has been corrected.]
Einbeck, J., Evers, L. & Hinchliff, K. (2010): Data compression and regression based on local principal curves. In A. Fink, B. Lausen, W. Seidel, and A. Ultsch (Eds), Advances in Data Analysis, Data Handling, and Business Intelligence, Heidelberg, pp. 701--712, Springer [available on request from the authors].
Sofroniou, N., Hoad, D., & Einbeck, J. (2008): League tables for literacy survey data based on random effect models. In Eilers, Paul (Ed): Proceedings of the 23rd International Workshop on Statistical Modelling, Utrecht, pages 402-405.
Einbeck, J. (2007): Analyzing traffic data with function-free smoothing methods: Approaches and challenges. Cemapre Conference on Advances in Semiparametric Methods and Applications (ASMA), Lisbon, 2007.
Einbeck, J., Augustin, T., & Singer, J.M. (2007): Smoothing, sampling, and Basu's elephants . In del Castillo et al. (Eds): Proceedings of the 22nd International Workshop on Statistical Modelling, Barcelona, pages 245-248.
Newell, J. & Einbeck, J. (2007): A comparative study of nonparametric derivative estimators. In del Castillo et al. (Eds): Proceedings of the 22nd International Workshop on Statistical Modelling, Barcelona, pages 453-456.
Einbeck, J., & Tutz, G. (2006): The fitting of multifunctions: an approach to nonparametric multimodal regression . In Rizzi, A. & Vichi, M. (Eds): COMPSTAT 2006 - Proceedings in Computational Statistics, Physica-Verlag, Heidelberg, pages 1251-1258.
Sofroniou, N., Einbeck, J., & Hinde, J. (2006): Analyzing Irish suicide rates with mixture models . In Hinde, J. et al. (Eds): IWSM 2006: Proceedings of the 21st International Workshop on Statistical Modelling, Galway, pages 474-481.
Newell, J., Einbeck, J., Madden, N. & McMilian, K. (2005): Model free endurance markers based on the second derivative of blood lactate curves. In: Francis, A.R., Matawie, K.M., Oshlack, A., & Smyth, G.K. (Eds.): Statistical Solutions to Modern Problems. Univ. of Western Sydney, Sydney. (Proceedings of the 20th International Workshop on Statistical Modelling) , pages 357-364.
Einbeck, J., Tutz, G. & Evers, L. (2005): Exploring Multivariate Data Structures with Local Principal Curves. In: Weihs, C. and Gaul, W. (Eds.): Classification - The Ubiquitous Challenge. Springer, Heidelberg. (Proceedings of the 28th Annual Conference of the GfKl 2004.) , pages 256-263.

Edited volumes

Einbeck, J., Hinde, J., Ingrassia, S., Lin, T.-I. and McNicholas, P. D. (2019). 4th Special Issue on Advances in Mixture models, Computational Statistics and Data Analysis, Volume 132.
Ainsbury, E.A., Calle, M.L., Cardis, E., Einbeck, J., Gómez, G., and Puig, P. (2017). Extended Abstracts Fall 2015 -- Biomedical Big Data; Statistics for Low Dose Radiation Research. Springer/Birkhäuser, ISBN 978-3-319-55639-0.
Hinde, J., Einbeck, J., & Newell, J. (2006): IWSM 2006: Proceedings of the 21th International Workshop on Statistical Modelling, Galway, ISBN 1-86220-180-3.

Contributions in edited volumes

Basu, T., Matthias, M.C.M and Einbeck, J. (2021) Bayesian Adaptive Selection Under Prior Ignorance , In: Vasile, M., and Qualgiarella, D. (Eds): Advances in Uncertainty Quantification and Optimization Under Uncertainty with Aerospace Applications. Cham: Springer, pp. 365-378.
Errington, A., Einbeck, J. and Cumming, J. (2021), Estimating Exposure Fraction from Radiation Biomarkers: A Comparison of Frequentist and Bayesian Approaches. In: Vasile, M., and Qualgiarella, D. (Eds): Advances in Uncertainty Quantification and Optimization Under Uncertainty with Aerospace Applications. Cham: Springer, pp. 393-405.
Basu, T., Einbeck, J., Troffaes, M. C. M. (2021), Uncertainty quantification in lasso-type regularization problems. In: Vasile, M (Editor): Optimization Under Uncertainty with Applications to Aerospace Engineering, pp. 81-109.
Einbeck, J., Hinde, J., Ingrassia, S., Lin, T.-I., and McNicholas, P.D. (2019) Editorial for the 4th Special Issue on advances in mixture models. Computational Statistics and Data Analysis, DOI: 10.1016/j.csda.2018.12.006.
Einbeck, J., Ainsbury, E., Barnard, S., Oliveira, M., Manning, G., Puig, P. and Badie, C. (2017). On the use of random effect models for radiation biodosimetry . In: Ainsbury, E.A. et al. (Eds): Extended Abstracts Fall 2015 -- Biomedical Big Data; Statistics for Low Dose Radiation Research. Springer, Birkhäuser.
Julian, B.R., Foulger, G.R., Hatfield, O., Jackson, S.E., Simpson, E., Einbeck, J., and Moore, A. (2015). Hotspots in hindsight. In: Foulger, G.R., Lustrino, M., and King, S.D. (Eds.): The Interdisciplinary Earth: A Volume in Honor of Don L. Anderson: Geological Society of America Special Paper 514 and American Geophysical Union Special Publication 71, p. 105–121, doi:10.1130/2015.2514(08).
Einbeck, J., Evers, L., and Bailer-Jones, C. (2008). Representing complex data using localized principal components with application to astronomical data . In: Gorban, A, Kegl, B, Wunsch, D, & Zinovyev, A: Principal Manifolds for Data Visualization and Dimension Reduction ; Lecture Notes in Computational Science and Engineering 58, 180-204, ISSN/ISBN: 978-3-540-73749-0.

Others

Reissland, Nadja NN, Wood, R., Einbeck, Jochen and Lane, Alison, Testing Fetal Abilities: A Commentary on Studies Testing Prenatal Reactions to Light Stimulation. ISCIENCE-D-20-00489. Available at SSRN: https://ssrn.com/abstract=3569540 or http://dx.doi.org/10.2139/ssrn.3569540
Bonetti, D., Delbem, A., Leão, D., and Einbeck, J. (2019). Estimation of Distribution Algorithm for Protein Structure Prediction. arXiv:1901.01059 [q-bio.BM]
Almohaimeed, A. and Einbeck, J. (2017). Response Transformations for Random Effect and Variance Component Models. Vignette to R package boxcoxmix. On CRAN.
Einbeck, J., and Hinde, J. (2007): Nonparametric maximum likelihood estimation for random effect models in R. Vignette to R package npmlreg. On CRAN.
Einbeck, J. (2007): Designing a Postgraduate Training Week. For PGCert, unpublished.
Einbeck, J. (1999): Ein kleiner Kurs in HTML. Marketing Report Gesundheit 99 , 0.22-0.27. ISSN 0937-888X.
Ph.D. Thesis.

Last modified: Dec 2007