Staff profile

• Mixture models
• Nonparametric regression
• Principal curves
• Random effect modelling

• 2000: Associate Editor, Advances in Statistical Analysis:
• 2000: Associate Editor, Statistical Modelling:
• 2000: Member of the Executive Committee of the SMS: The Statistical Modelling Society (SMS) is an international society with the purpose of promoting and encouraging statistical modelling, and which organizes the annual conference "International Workshop on Statistical Modelling". I have been elected member of the SMS Executive Committee 2011-12 and 2015-18, and continue to be member on the Committee as the Representative of the WG for Communication

Chapter in book

• Basu, T., Einbeck, J., & Troffaes, M. C. (2021). Uncertainty Quantification in Lasso-Type Regularization Problems. In Optimization Under Uncertainty with Applications to Aerospace Engineering (81-109). Springer Verlag. https://doi.org/10.1007/978-3-030-60166-9_3
• Errington, A., Einbeck, J., & Cumming, J. (2021). Estimating Exposure Fraction from Radiation Biomarkers: A Comparison of Frequentist and Bayesian Approaches. In M. Vasile, & D. Quagliarella (Eds.), Advances in Uncertainty Quantification and Optimization Under Uncertainty with Aerospace Applications (393-405). Springer Verlag. https://doi.org/10.1007/978-3-030-80542-5_24
• Einbeck, J., Ainsbury, E., Barnard, S., Oliveira, M., Manning, G., Puig, P., & Badie, C. (2017). On the Use of Random Effect Models for Radiation Biodosimetry. In E. Ainsbury, M. Calle, E. Cardis, J. Einbeck, G. Gómez, & P. Puig (Eds.), Extended abstracts Fall 2015 : Biomedical Big Data ; Statistics for Low Dose Radiation Research (89-94). Springer Verlag. https://doi.org/10.1007/978-3-319-55639-0_15
• Julian, B., Foulger, G., Hatfield, O., Jackson, S., Simpson, E., Einbeck, J., & Moore, A. (2015). Hotspots in Hindsight. In G. Foulger, M. Lustrino, & S. King (Eds.), The Interdisciplinary Earth: A Volume in Honor of Don L. Anderson (105-121). The Geological Society of America / AGU. https://doi.org/10.1130/2015.2514%2808%29
• Einbeck, J., Evers, L., & Bailer-Jones, C. (2008). Representing complex data using localized principal components with application to astronomical data. In A. Gorban, B. Kegl, D. Wunsch, & A. Zinovyev (Eds.), Lecture Notes in Computational Science and Engineering (180-204). Springer Verlag. https://doi.org/10.1007/978-3-540-73750-6_7

Conference Paper

• Zhang, Y., & Einbeck, J. (2022). Simultaneous linear dimension reduction and clustering with flexible variance matrices. In N. Torelli, R. Bellio, & V. Muggeo (Eds.), Proceedings of the 36th International Workshop on Statistical Modelling (612-617)
• Basu, T., Troffaes, M. C., & Einbeck, J. (2021). Bayesian Adaptive Selection Under Prior Ignorance. In M. Vasile, & D. Quagliarella (Eds.), . https://doi.org/10.1007/978-3-030-80542-5_22
• Basu, T., Troffaes, M. C., & Einbeck, J. (2020). Binary Credal Classification Under Sparsity Constraints. In M. Lesot, S. Vieira, M. Z. Reformat, J. P. Carvalho, A. Wilbik, B. Bouchon-Meunier, & R. R. Yager (Eds.), Information processing and management of uncertainty in knowledge-based systems : 18th International Conference, IPMU 2020, Lisbon, Portugal, June 15–19, 2020, proceedings, Part II (82-95). https://doi.org/10.1007/978-3-030-50143-3_7
• Basu, T., Einbeck, J., & Troffaes, M. (2020). A sensitivity analysis and error bounds for the adaptive lasso. In I. Irigoien, D. -. Lee, J. Martinez-Minaya, & M. X. Rodriguez-Alvarez (Eds.), Proceedings of the 35th International Workshop on Statistical Modelling (278-281)
• Almohaimeed, A., & Einbeck, J. (2018). Box-Cox response transformations for random effect models. In Proceedings of the 33rd International Workshop on Statistical Modelling (1-6)
• Einbeck, J., Gray, E., Sofroniou, N., Marques da Silva Junior, A., & Gledhill, J. (2017). Con fidence intervals for posterior intercepts, with application to the PIAAC literacy survey. In M. Grzegorczyk, & G. Ceoldo (Eds.), Proceedings of the 32nd International Workshop on Statistical Modelling : Groningen, Netherlands, 3-7 July, 2017 (217-222)
• Einbeck, J., & Wilson, P. (2016). A diagnostic plot for assessing model fit in count data models. In J. F. Dupuy, & J. Josse (Eds.), Proceedings of the 31st International Workshop on Statistical Modelling. July 4-8, 2016, Rennes, France (103-108)
• da Silva-Junior, A., Einbeck, J., & Craig, P. (2016). Gradient test for generalised linear models with random effects. In J. F. Dupuy, & J. Josse (Eds.), Proceedings of the 31st International Workshop on Statistical Modelling. July 4-8, 2016, Rennes, France (213-218)
• Wilson, P., & Einbeck, J. (2016). On statistical testing and mean parameter estimation for zero-modification in count data regression. In J. F. Dupuy, & J. Josse (Eds.), Proceedings of the 31st International Workshop on Statistical Modelling. July 4-8, 2016, Rennes, France (327-332)
• Wilson, P., & Einbeck, J. (2015). A simple and intuitive test for number-inflation or number-deflation. In H. Wagner, & H. Friedl (Eds.), Proceedings of the 30th International Workshop on Statistical Modelling. Linz, Austria, 6-10 July 2015 (299-302)
• Bonetti, D., Delbem, A., & Einbeck, J. (2014). Bivariate Estimation of Distribution Algorithms for Protein Structure Prediction. In T. Kneib, F. Sobotka, J. Fahrenholz, & H. Irmer (Eds.), 29th International Workshop on Statistical Modelling, 14-18 July 2014, Göttingen, Germany ; proceedings (15-18)
• Tsiftsi, T., Jermyn, I., & Einbeck, J. (2014). Bayesian shape modelling of cross-sectional geological data. In K. Thomas, S. Fabian, F. Jan, & I. Henriette (Eds.), 29th International Workshop on Statistical Modelling, 14-18 July 2014, Göttingen, Germany ; proceedings (161-164)
• Einbeck, J., & Bonetti, D. (2014). A study of online and blockwise updating of the EM algorithm for Gaussian mixtures. In T. Kneib, F. Sobotka, J. Fahrenholz, & H. Irmer (Eds.), 29th International Workshop on Statistical Modelling, 14-18 July 2014, Göttingen, Germany ; proceedings (35-38)
• Einbeck, J., Isaac, B., Evers, L., & Parente, A. (2012). Penalized regression on principal manifolds with application to combustion modelling. In A. Komarek, & S. Nagy (Eds.), 27th International Workshop on Statistical Modelling, 16-20 July 2012, Prague, Czech Republic ; proceedings (117-122)
• Lawson, A., & Einbeck, J. (2012). Generative linear mixture modelling. In A. Komarek, & S. Nagy (Eds.), 27th International Workshop on Statistical Modelling, 16-20 July 2012, Prague, Czech Republic ; proceedings (595-600)
• Kalantan, Z., & Einbeck, J. (2012). On the computation of the correlation integral for fractal dimension estimation. . https://doi.org/10.1109/icssbe.2012.6396531
• Taylor, J., & Einbeck, J. (2011). Multivariate regression smoothing through the 'fallling net'. In D. Conesa, A. Forte, A. Lopez-Quilez, & F. Munoz (Eds.), 26th International Workshop on Statistical Modelling, 5-11 July 2011, Valencia, Spain ; proceedings (597-602)
• Einbeck, J., & Evers, L. (2010). Localized regression on principal manifolds. In A. Bowman (Ed.),
• Taylor, J., & Einbeck, J. (2010). Strategies for local smoothing in high dimensions: using density thresholds and adapted GCV. In A. Bowman (Ed.),
• Einbeck, J., Evers, L., & Hinchliff, K. (2010). Data compression and regression based on local principal curves. In A. Fink, B. Lausen, W. Seidel, & A. Ultsch (Eds.), Advances in data analysis, data handling and business intelligence (701-712). https://doi.org/10.1007/978-3-642-01044-6_64
• Zayed, M., & Einbeck, J. (2010). Constructing Economic Summary Indexes via Principal Curves. In Y. Lechevallier, & G. Saporta (Eds.),
• Sofroniou, N., Hoad, D., & Einbeck, J. (2008). League tables for literacy survey data based on random effect models. In P. Eilers (Ed.), 23rd International Workshop on Statistical Modelling, 7-11 July 2008, Utrecht ; proceedings (402-405)
• Einbeck, J., Augustin, T., & Singer, J. M. (2007). Smoothing, Sampling, and Basu's elephants. In J. del Castillo, A. Espinal, & P. Puig (Eds.),
• Newell, J., & Einbeck, J. (2007). A comparative study of nonparametric derivative estimators. In J. del Castillo, A. Espinal, & P. Puig (Eds.),
• Einbeck, J., & Tutz, G. (2006). The fitting of multifunctions: an approach to nonparametric multimodal regression. In A. Rizzi, & M. Vichi (Eds.), COMPSTAT 2006 : proceedings in computational statistics, 17th symposium held in Rome, Italy, 2006 (1251-1258)
• Sofroniou, N., Einbeck, J., Hinde, J., & Newell, J. (2006). Analyzing Irish suicide rates with mixture models. In Proceedings of the 21st International Workshop on Statistical Modelling: IWSM 2006, 3-7 July 2006, Galway, Ireland (474-481)
• Newell, J., Einbeck, J., Madden, N., & McMillan, K. (2005). Model free endurance markers based on the second derivative of blood lactate curves. In A. R. Francis, K. M. Matawie, A. Oshlack, & G. K. Smyth (Eds.), Statistical solutions to modern problems ; proceedings of the 20th International Workshop on Statistical Modelling. Sydney, Australia, July 10-15, 2005 (357-364)
• Einbeck, J., Tutz, G., & Evers, L. (2005). Exploring Multivariate Data Structures with Local Principal Curves. In C. Weihs, & W. Gaul (Eds.), Proceedings of the 28th Annual Conference of the Gesellschaft für Klassifikation, 9-11 March 2004, University of Dortmund (256-263)

Doctoral Thesis

• Einbeck, J. (2003). Local Smoothing Methods for the Analysis of Multivariate Complex Data Structures. (Thesis). Institut fuer Statistik, LMU Muenchen. Retrieved from https://durham-repository.worktribe.com/output/1647400

Edited book

• Ainsbury, E., Calle, M., Cardis, E., Einbeck, J., Gómez, G., & Puig, P. (Eds.). (2017). Extended Abstracts Fall 2015. Biomedical Big Data; Statistics for Low Dose Radiation Research. Springer Verlag
• Hinde, J., Einbeck, J., & Newell, J. (Eds.). (2006). Proceedings of the 21st International Workshop on Statistical Modelling. Galway, Ireland, 3-7 July 2006. National University of Ireland, Galway

Journal Article

• Zhang, Y., & Einbeck, J. (2024). A Versatile Model for Clustered and Highly Correlated Multivariate Data. Journal of statistical theory and practice, 18(1), Article 5. https://doi.org/10.1007/s42519-023-00357-0
• Reissland, N., Ustun, B., & Einbeck, J. (2024). The effects of lockdown during the COVID-19 pandemic on fetal movement profiles. BMC Pregnancy and Childbirth, 24(1), 56. https://doi.org/10.1186/s12884-024-06259-8
• Ameijeiras-Alonso, J., & Einbeck, J. (2023). A fresh look at mean-shift based modal clustering. Advances in Data Analysis and Classification, https://doi.org/10.1007/s11634-023-00575-1
• Almohaimeed, A., & Einbeck, J. (2023). A Sequential Cross-Sectional Analysis Producing Robust Weekly COVID-19 Rates for South East Asian Countries. Viruses, 15(7), Article 1572. https://doi.org/10.3390/v15071572
• Reissland, N., Matthewson, J., & Einbeck, J. (2023). Association between Hyperemesis Gravidarum in pregnancy on postnatal ability of infants to attend to a play task with their mother. Infant Behavior & Development, 71, https://doi.org/10.1016/j.infbeh.2023.101823
• Bar-Lev, S. K., Batsidis, A., Einbeck, J., Liu, X., & Ren, P. (2023). Cumulant-Based Goodness-of-Fit Tests for the Tweedie, Bar-Lev and Enis Class of Distributions. Mathematics, 11(7), Article 1603. https://doi.org/10.3390/math11071603
• Hernández, A., Endesfelder, D., Einbeck, J., Puig, P., Benadjaoud, M. A., Higueras, M., …Barquinero, J. F. (2023). Biodose Tools: an R shiny application for biological dosimetry. International Journal of Radiation Biology, 99(9), https://doi.org/10.1080/09553002.2023.2176564
• Basu, T., Troffaes, M. C., & Einbeck, J. (2023). A robust Bayesian analysis of variable selection under prior ignorance. Sankhya A - Mathematical Statistics and Probability, 85(1), 1014-1057. https://doi.org/10.1007/s13171-022-00287-2
• Almohaimeed, A., & Einbeck, J. (2022). Response transformations for random effect and variance component models. Statistical Modelling, 22(4), 297-326. https://doi.org/10.1177/1471082x20966919
• Errington, A., Einbeck, J., Cumming, J., Rössler, U., & Endesfelder, D. (2022). The effect of data aggregation on dispersion estimates in count data models. International Journal of Biostatistics, 18(1), 183-202. https://doi.org/10.1515/ijb-2020-0079
• Almohaimeed, A., Einbeck, J., Qarmalah, N., & 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), https://doi.org/10.3390/ijerph192214960
• 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. https://doi.org/10.1080/1743727x.2021.1882416
• Tolley, C. L., Watson, N. W., Heed, A., Einbeck, J., Medows, S., Wood, 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), Article 86. https://doi.org/10.1186/s12911-022-01828-3
• Reissland, N., Einbeck, J., Wood, R., & Lane, A. (2021). Effects of maternal mental health on prenatal movement profiles in twins and singletons. Acta Paediatrica: Nurturing the Child, 110(9), 2553-2558. https://doi.org/10.1111/apa.15903
• Tolley, C., Watson, N., Heed, A., Einbeck, J., Medows, S., Wood, L., …Slight, S. (2021). The Impact of a Bedside Medication Scanning Device on Administration Errors in the Hospital Setting: A Prospective Observational Study. International Journal of Pharmacy Practice, 29(Supplement_1), i9. https://doi.org/10.1093/ijpp/riab016.011
• 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. https://doi.org/10.17713/ajs.v50i1.921
• 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, Article 105227. https://doi.org/10.1016/j.earlhumdev.2020.105227
• 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, 34(9), Article 2058010. https://doi.org/10.1142/s0218001420580100
• 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 Gynecology and Obstetrics, 302(1), 65-75. https://doi.org/10.1007/s00404-020-05571-w
• Endesfelder, D., Kulka, U., Einbeck, J., & Oestreicher, U. (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. https://doi.org/10.1080/09553002.2020.1829152
• Kalantan, Z. I., & Einbeck, J. (2019). Quantile-Based Estimation of the Finite Cauchy Mixture Model. Symmetry, 11(9), Article 1186. https://doi.org/10.3390/sym11091186
• Wilson, P., & Einbeck, J. (2019). A new and intuitive test for zero modification. Statistical Modelling, 19(4), 341--361. https://doi.org/10.1177/1471082x18762277
• 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), https://doi.org/10.1371/journal.pone.0207464
• Qarmalah, N. M., Einbeck, J., & Coolen, F. P. (2018). k-Boxplots for mixture data. Statistical Papers, 59(2), 513-528. https://doi.org/10.1007/s00362-016-0774-7
• Marques da Silva Júnior, A. H., Einbeck, J., & Craig, P. S. (2018). Fisher information under Gaussian quadrature models. Statistica Neerlandica, 72(2), 74-89. https://doi.org/10.1111/stan.12116
• Einbeck, J., & Meintanis, S. (2017). Self–consistency–based tests for bivariate distributions. Journal of statistical theory and practice, 11(3), 478-492. https://doi.org/10.1080/15598608.2017.1318098
• Qarmalah, N. M., Einbeck, J., & Coolen, F. P. (2017). Mixture Models for Prediction from Time Series, with Application to Energy Use Data. Archives of data science. Series A, 2(1), 1-15. https://doi.org/10.5445/ksp/1000058749/07
• Ainsbury, E. A., Higueras, M., Puig, P., Einbeck, J., Samaga, D., Barquinero, J. F., …Woda, C. (2017). Uncertainty of fast biological radiation dose assessment for emergency response scenarios. International Journal of Radiation Biology, 93(1), 127-135. https://doi.org/10.1080/09553002.2016.1227106
• Jackson, S. E., Einbeck, J., Kasim, A., & 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., Puig, P., & Rothkamm, K. (2016). Zero-inflated regression models for radiation-induced chromosome aberration data: A comparative study. Biometrical Journal, 58(2), 259-279. https://doi.org/10.1002/bimj.201400233
• Einbeck, J., Jackson, S. E., & Kasim, A. (2015). A summer with genes: Simple disease classification from microarray data. Mathematics today, 51(4), 186-188
• Einbeck, J., & Zayed, M. (2014). Some asymptotics for localized principal components and curves. Communications in Statistics - Theory and Methods, 43(8), 1736-1749. https://doi.org/10.1080/03610926.2012.673676
• Back, J., Barker, G., Boyd, S., Einbeck, J., Haigh, M., Morgan, B., …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(3), Article 2832. https://doi.org/10.1140/epjc/s10052-014-2832-4
• Einbeck, J. (2013). Discussion of ‘Beyond mean regression’. Statistical Modelling, 13(4), 349-354. https://doi.org/10.1177/1471082x13494526
• Meintanis, S., & Einbeck, J. (2013). Validation tests for semi-parametric models. Journal of Statistical Computation and Simulation, 85(1), 131-146. https://doi.org/10.1080/00949655.2013.806922
• Taylor, J., & Einbeck, J. (2013). Challenging the curse of dimensionality in multivariate local linear regression. Computational Statistics, 28(3), 955-976. https://doi.org/10.1007/s00180-012-0342-0
• Einbeck, J., & Taylor, J. (2013). A number-of-modes reference rule for density estimation under multimodality. Statistica Neerlandica, 67(1), 54-66. https://doi.org/10.1111/j.1467-9574.2012.00531.x
• Meintanis, S., & Einbeck, J. (2012). Goodness-of-fit tests in semi-linear models. Statistics and Computing, 22(4), 967-979. https://doi.org/10.1007/s11222-011-9266-8
• Einbeck, J., & Dwyer, J. (2011). Using principal curves to analyse traffic patterns on freeways. Transportmetrica, 7(3), 229-246. https://doi.org/10.1080/18128600903500110
• Einbeck, J. (2011). Bandwidth Selection for Mean-shift based Unsupervised Learning Techniques: a Unified Approach via Self-coverage. Journal of pattern recognition research, 6(2), 175-192. https://doi.org/10.13176/11.288
• Einbeck, J., Evers, L., & Powell, B. (2010). Data compression and regression through local principal curves and surfaces. International Journal of Neural Systems, 20(3), 177-192. https://doi.org/10.1142/s0129065710002346
• Fried, R., Einbeck, J., & Gather, U. (2007). Weighted Repeated Median Smoothing and Filtering. Journal of the American Statistical Association, 102(480), 1300-1308. https://doi.org/10.1198/016214507000001166
• Einbeck, J., Hinde, J., & Darnell, R. (2007). A new package for fitting random effect models. R news, 7(1), 26-30
• Einbeck, J., & Augustin, T. (2007). On design-weighted local fitting and its relation to the Horvitz-Thompson estimator. Statistica sinica, 19(1), 103-123
• 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 Sciences, 25(12), 1403-1409. https://doi.org/10.1080/02640410601128922
• 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, 55(4), 461-475. https://doi.org/10.1111/j.1467-9876.2006.00547.x
• Einbeck, J., & Hinde, J. (2006). A note on NPML estimation for exponential family regression models with unspecified dispersion parameter. Austrian Journal of Statistics, 35(2&3), 233-243
• Einbeck, J., Tutz, G., & Evers, L. (2005). Local Principal Curves. Statistics and Computing, 15(4), 301-313. https://doi.org/10.1007/s11222-005-4073-8
• Einbeck, J. (2004). Local fitting with a power basis. Revstat Statistical Journal, 2(2), 102-126
• Einbeck, J., Andre, C. D., & Singer, J. M. (2004). Local Smoothing with Robustness against outlying Predictors. Environmetrics, 15(6), 541-554. https://doi.org/10.1002/env.644
• 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(1), 69-74
• Einbeck, J., & Kauermann, G. (2003). Online Monitoring with Local Smoothing Methods and Adaptive Ridging. Journal of Statistical Computation and Simulation, 73(12), 913-929. https://doi.org/10.1080/0094965031000104332
• Einbeck, J. (2003). Multivariate Local Fitting with General Basis Functions. Computational Statistics, 18(2), 185-203. https://doi.org/10.1007/s001800300140

Presentation

• Basu, T., Einbeck, J., Troffaes, M. C., & Forbes, A. (2019, July). Robust uncertainty quantification for measurement problems with limited information. Paper presented at ISIPTA 2019, Ghent, Belgium
• Basu, T., Einbeck, J., & Troffaes, M. C. (2019, December). A sensitivity analysis of adaptive lasso. Paper presented at Innovations in Data and Statistical Sciences (INDSTATS 2019), Mumbai, India

Report

• Menzies, V., Cramman, H., Eerola, P., Hugill-Jones, J., Akhter, N., & Einbeck, J. (2022). Parents and Children Together (PACT) Evaluation Report. [No known commissioning body]