Staff profile
Overview
Professor Jochen Einbeck
Director of Durham Biostatistics Unit, Professor, Statistics
Affiliation | Telephone |
---|---|
Director of Durham Biostatistics Unit, Professor, Statistics in the Department of Mathematical Sciences | +44 (0) 191 33 43125 |
DRMC Co-Director (Health Data Science) in the Faculty of Social Sciences and Health | |
Co-Director (Biostatistics & Apprenticeships) in the Durham Research Methods Centre |
Research interests
- Mixture models
- Nonparametric regression
- Principal curves
- Random effect modelling
Esteem Indicators
- 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
Publications
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
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
Supervision students
Deimante Baguckaite
Research Postgraduate
Emmanuel Ifeh
4S
Germaine Uwimpuhwe
Research Assistant
Shrog Albalawi
1S
Yingjuan Zhang
3S
Yuzheng Zhang
Research Postgraduate – Bioengineering Node