Staff profile

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

• 2000: Associate Editor, Statistical Modelling:
• 2000: Associate Editor, Advances in Statistical Analysis:
• 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

• A Distance-Based Statistic for Goodness-of-Fit Assessment
  
  Jayakumari, D., Einbeck, J., Hinde, J., & Moral, R. A. (2024). A Distance-Based Statistic for Goodness-of-Fit Assessment. In J. Einbeck, H. Maeng, E. Ogundimu, & K. Perrakis (Eds.), Developments in Statistical Modelling (pp. 263-268). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-65723-8_40
• Estimating Dose and Time of Exposure from a Protein-Based Radiation Biomarker
  
  Cai, Y., Einbeck, J., Barnard, S., & Ainsbury, E. (2024). Estimating Dose and Time of Exposure from a Protein-Based Radiation Biomarker. In Developments in Statistical Modelling (pp. 239-245). Springer. https://doi.org/10.1007/978-3-031-65723-8_37
• Tools for Assessing Goodness of Fit of GLMs: Case Studies in Entomology
  
  Jayakumari, D., Hinde, J., Einbeck, J., & Moral, R. A. (2024). Tools for Assessing Goodness of Fit of GLMs: Case Studies in Entomology. In Modelling Insect Populations in Agricultural Landscapes (pp. 211-235). Springer International Publishing. https://doi.org/10.1007/978-3-031-43098-5_11
• Elicitation of Priors for Intervention Effects in Educational Trial Data
  
  Zhang, Q., Uwimpuhwe, G., Vallis, D., Singh, A., Coolen-Maturi, T., & Einbeck, J. (2024). Elicitation of Priors for Intervention Effects in Educational Trial Data. In J. Einbeck, H. Maeng, E. Ogundimu, & K. Perrakis (Eds.), Developments in Statistical Modelling (pp. 28-33). Springer. https://doi.org/10.1007/978-3-031-65723-8_5
• Uncertainty Quantification in Lasso-Type Regularization Problems
  
  Basu, T., Einbeck, J., & Troffaes, M. C. (2021). Uncertainty Quantification in Lasso-Type Regularization Problems. In Optimization Under Uncertainty with Applications to Aerospace Engineering (pp. 81-109). Springer Verlag. https://doi.org/10.1007/978-3-030-60166-9_3
• Estimating Exposure Fraction from Radiation Biomarkers: A Comparison of Frequentist and Bayesian Approaches
  
  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 (pp. 393-405). Springer Verlag. https://doi.org/10.1007/978-3-030-80542-5_24
• On the Use of Random Effect Models for Radiation Biodosimetry
  
  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. (pp. 89-94). Springer Verlag. https://doi.org/10.1007/978-3-319-55639-0_15
• Hotspots in Hindsight
  
  Julian, B. R., Foulger, G. R., Hatfield, O., Jackson, S. E., Simpson, E., Einbeck, J., & Moore, A. (2015). Hotspots in Hindsight. In The Interdisciplinary Earth: A Volume in Honor of Don L. Anderson (pp. 105-121). The Geological Society of America / AGU. https://doi.org/10.1130/2015.2514%2808%29
• Representing complex data using localized principal components with application to astronomical data.
  
  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. (pp. 180-204). Springer-Verlag. https://doi.org/10.1007/978-3-540-73750-6_7

Conference Paper

• Using linear mixed models to compare a self-assessed frailty score with clinician assessed scores in patients approaching major surgery
  
  Sayari, M., Durrand, J., Taylor, C., Einbeck, J., Kharatikoopaei, E., Craig, J., & Griffiths, N. (2024, July 15). Using linear mixed models to compare a self-assessed frailty score with clinician assessed scores in patients approaching major surgery. Presented at International Workshop on Statistical Modelling, Durham.
• A multilevel multivariate response model for data with latent structures
  
  Zhang, Y., Einbeck, J., & Drikvandi, R. (2023, July 21). A multilevel multivariate response model for data with latent structures. Presented at The 37th International Workshop on Statistical Modelling, Dortmund, Germany.
• Individual participant data meta-analysis: pooled effect of EEF funded educational trials on low baseline attaining group
  
  Uwimpuhwe, G., Singh, A., Akhter, N., Ashraf, B., Coolen-Maturi, T., Robinson, T., Higgins, S., & Einbeck, J. (2023). Individual participant data meta-analysis: pooled effect of EEF funded educational trials on low baseline attaining group. In E. Bergherr, A. Groll, & A. Mayr (Eds.), 37th International Workshop on Statistical Modelling - Proceedings book (pp. 640-643). Statistical Modelling Society.
• A Robust Bayesian Approach for Causal Inference Problems
  
  Basu, T., Troffaes, M. C. M., & Einbeck, J. (2023). A Robust Bayesian Approach for Causal Inference Problems. In Symbolic and Quantitative Approaches to Reasoning with Uncertainty (pp. 359-371). Springer Nature. https://doi.org/10.1007/978-3-031-45608-4_27
• Simultaneous linear dimension reduction and clustering with flexible variance matrices
  
  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 (pp. 612-617). EUT Edizioni Università di Trieste.
• Bayesian Adaptive Selection Under Prior Ignorance
  
  Basu, T., Troffaes, M. C., & Einbeck, J. (2021). Bayesian Adaptive Selection Under Prior Ignorance. In M. Vasile & D. Quagliarella (Eds.), Space Technology Proceedings (pp. 365-378). Springer Verlag. https://doi.org/10.1007/978-3-030-80542-5_22
• A sensitivity analysis and error bounds for the adaptive lasso
  
  Basu, T., Einbeck, J., & Troffaes, M. (2020). A sensitivity analysis and error bounds for the adaptive lasso. In I. Irigoien, D. .-J. Lee, J. Martinez-Minaya, & M. X. Rodriguez-Alvarez (Eds.), Proceedings of the 35th International Workshop on Statistical Modelling. (pp. 278-281). Universidad del Pais Vasco.
• Binary Credal Classification Under Sparsity Constraints
  
  Basu, T., Troffaes, M. C., & Einbeck, J. (2020). Binary Credal Classification Under Sparsity Constraints. In M.-J. 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. (pp. 82-95). Springer Verlag. https://doi.org/10.1007/978-3-030-50143-3_7
• Robust uncertainty quantification for measurement problems with limited information
  
  Basu, T., Einbeck, J., Troffaes, M. C., & Forbes, A. (2019, July 3 – 2019, July 6). Robust uncertainty quantification for measurement problems with limited information [Conference paper]. Presented at ISIPTA 2019, Ghent, Belgium.
• A sensitivity analysis of adaptive lasso
  
  Basu, T., Einbeck, J., & Troffaes, M. C. (2019). A sensitivity analysis of adaptive lasso [Conference paper]. Presented at Innovations in Data and Statistical Sciences (INDSTATS 2019), Mumbai, India.
• Box-Cox response transformations for random effect models
  
  Almohaimeed, A., & Einbeck, J. (2018). Box-Cox response transformations for random effect models. In Proceedings of the 33rd International Workshop on Statistical Modelling (pp. 1-6). University of Bristol.
• Con fidence intervals for posterior intercepts, with application to the PIAAC literacy survey
  
  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. (pp. 217-222). University of Groningen.
• Gradient test for generalised linear models with random effects
  
  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. (pp. 213-218). Statistical Modelling Society.
• A diagnostic plot for assessing model fit in count data models
  
  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. (pp. 103-108). Statistical Modelling Society.
• On statistical testing and mean parameter estimation for zero-modification in count data regression
  
  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. (pp. 327-332). Statistical Modelling Society.
• A simple and intuitive test for number-inflation or number-deflation
  
  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. (pp. 299-302). Statistical Modelling Society.
• A study of online and blockwise updating of the EM algorithm for Gaussian mixtures
  
  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. (pp. 35-38). Statistical Modelling Society.
• Bayesian shape modelling of cross-sectional geological data
  
  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. (pp. 161-164). Statistical Modelling Society.
• Bivariate Estimation of Distribution Algorithms for Protein Structure Prediction
  
  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. (pp. 15-18). Statistical Modelling Society.
• On the computation of the correlation integral for fractal dimension estimation
  
  Kalantan, Z., & Einbeck, J. (2012). On the computation of the correlation integral for fractal dimension estimation. In IEEE conference publications (pp. 80-85). https://doi.org/10.1109/icssbe.2012.6396531
• Penalized regression on principal manifolds with application to combustion modelling
  
  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. (pp. 117-122). Statistical Modeling Society.
• Generative linear mixture modelling
  
  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. (pp. 595-600). Statistical Modeling Society.
• Multivariate regression smoothing through the 'fallling net'
  
  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. (pp. 597-602). Statistical Modelling Society.
• Localized regression on principal manifolds
  
  Einbeck, J., & Evers, L. (2010). Localized regression on principal manifolds (A. Bowman, Ed.). University of Glasgow.
• Data compression and regression based on local principal curves
  
  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. (pp. 701-712). Springer Verlag. https://doi.org/10.1007/978-3-642-01044-6_64
• Strategies for local smoothing in high dimensions: using density thresholds and adapted GCV
  
  Taylor, J., & Einbeck, J. (2010). Strategies for local smoothing in high dimensions: using density thresholds and adapted GCV (A. Bowman, Ed.). University of Glasgow.
• Constructing Economic Summary Indexes via Principal Curves
  
  Zayed, M., & Einbeck, J. (2010). Constructing Economic Summary Indexes via Principal Curves (Y. Lechevallier & G. Saporta, Eds.). Springer Verlag.
• League tables for literacy survey data based on random effect models
  
  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. (pp. 402-405). Statistical Modelling Society.
• A comparative study of nonparametric derivative estimators.
  
  Newell, J., & Einbeck, J. (2007). A comparative study of nonparametric derivative estimators. In J. del Castillo, A. Espinal, & P. Puig (Eds.), Proceedings of the IWSM (pp. 453-456). IDESCAT.
• Smoothing, Sampling, and Basu's elephants
  
  Einbeck, J., Augustin, T., & Singer, J. M. (2007). Smoothing, Sampling, and Basu’s elephants. In J. del Castillo, A. Espinal, & P. Puig (Eds.), Proceedings of the IWSM (pp. 245-248). IDESCAT.
• The fitting of multifunctions: an approach to nonparametric multimodal regression
  
  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. (pp. 1251-1258). Physica-Verlag.
• Analyzing Irish suicide rates with mixture models
  
  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. (pp. 474-481). National University of Ireland.
• Model free endurance markers based on the second derivative of blood lactate curves
  
  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. (pp. 357-364). Statistical Modelling Society.
• Exploring Multivariate Data Structures with Local Principal Curves
  
  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. (pp. 256-263). Springer Verlag.

Conference Proceeding

• Proceedings of the 38th International Workshop on Statistical Modelling
  
  Einbeck, J., Drikvandi, R., Karagiannis, G., Perrakis, K., & Zhang, Q. (Eds.). (2024). Proceedings of the 38th International Workshop on Statistical Modelling. Durham University.

Doctoral Thesis

• Local Smoothing Methods for the Analysis of Multivariate Complex Data Structures
  
  Einbeck, J. (2003). Local Smoothing Methods for the Analysis of Multivariate Complex Data Structures [Thesis]. Institut fuer Statistik, LMU Muenchen. http://www.maths.dur.ac.uk/~dma0je/Thesis/

Edited book

• Developments in Statistical Modelling
  
  Einbeck, J., Maeng, H., Ogundimu, E., & Perrakis, K. (Eds.). (2024). Developments in Statistical Modelling. Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-65723-8
• Extended Abstracts Fall 2015. Biomedical Big Data; Statistics for Low Dose Radiation Research
  
  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.
• Proceedings of the 21st International Workshop on Statistical Modelling. Galway, Ireland, 3-7 July 2006
  
  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

• Preferences for Psychological Therapy or Support Within an ARMS Psychological Therapies Trial: The Importance of Targeted Intervention for Unusual Sensory Experiences
  
  Hamilton, J., Singh, A., Gibbs, C., Barclay, N. A., Birkett, L., Boyle, C., Brandon, T., Dudley, R., Einbeck, J., Larry, V., Simpson, J., Dodgson, G., & Fernyhough, C. (2025). Preferences for Psychological Therapy or Support Within an ARMS Psychological Therapies Trial: The Importance of Targeted Intervention for Unusual Sensory Experiences. Early Intervention in Psychiatry, 19(4), Article e70035. https://doi.org/10.1111/eip.70035
• A two-level multivariate response model for data with latent structures
  
  Zhang, Y., Einbeck, J., & Drikvandi, R. (2025). A two-level multivariate response model for data with latent structures. Statistical Modelling. Advance online publication. https://doi.org/10.1177/1471082X241313024
• A fresh look at mean-shift based modal clustering
  
  Ameijeiras-Alonso, J., & Einbeck, J. (2024). A fresh look at mean-shift based modal clustering. Advances in Data Analysis and Classification, 18(4), 1067-1095. https://doi.org/10.1007/s11634-023-00575-1
• A Comparison of Threshold-Free Measures for Assessing the Effectiveness of Educational Interventions
  
  Einbeck, J., Coolen-Maturi, T., Uwimpuhwe, G., & Singh, A. (2024). A Comparison of Threshold-Free Measures for Assessing the Effectiveness of Educational Interventions. The Journal of Experimental Education. Advance online publication. https://doi.org/10.1080/00220973.2024.2405738
• Lack of effect of a parent‐delivered early language intervention: Evidence from a randomised controlled trial completed during COVID‐19
  
  Burgoyne, K., Hargreaves, S., Akhter, N., Cramman, H., Eerola, P., Einbeck, J., & Menzies, V. (2024). Lack of effect of a parent‐delivered early language intervention: Evidence from a randomised controlled trial completed during COVID‐19. JCPP Advances. Advance online publication, Article e12279. https://doi.org/10.1002/jcv2.12279
• Directed Clustering of Multivariate Data Based on Linear or Quadratic Latent Variable Models
  
  Zhang, Y., & Einbeck, J. (2024). Directed Clustering of Multivariate Data Based on Linear or Quadratic Latent Variable Models. Algorithms, 17(8), Article 358. https://doi.org/10.3390/a17080358
• A Versatile Model for Clustered and Highly Correlated Multivariate Data
  
  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
• The effects of lockdown during the COVID-19 pandemic on fetal movement profiles
  
  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
• A Sequential Cross-Sectional Analysis Producing Robust Weekly COVID-19 Rates for South East Asian Countries
  
  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
• Use of a targeted, computer/web-based guided self-help psychoeducation toolkit for distressing hallucinations (MUSE) in people with an at-risk mental state for psychosis: protocol for a randomised controlled feasibility trial
  
  Hamilton, J., Arnott, B., Aynsworth, C., Barclay, N. A., Birkett, L., Brandon, T., Dixon, L., Dudley, R., Einbeck, J., Gibbs, C., Kharatikoopaei, E., Simpson, J., Dodgson, G., & Fernyhough, C. (2023). Use of a targeted, computer/web-based guided self-help psychoeducation toolkit for distressing hallucinations (MUSE) in people with an at-risk mental state for psychosis: protocol for a randomised controlled feasibility trial. BMJ Open, 13(6), Article e076101. https://doi.org/10.1136/bmjopen-2023-076101
• Association between Hyperemesis Gravidarum in pregnancy on postnatal ability of infants to attend to a play task with their mother
  
  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 and Development, 71. https://doi.org/10.1016/j.infbeh.2023.101823
• Cumulant-Based Goodness-of-Fit Tests for the Tweedie, Bar-Lev and Enis Class of Distributions
  
  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
• Biodose Tools: an R shiny application for biological dosimetry
  
  Hernández, A., Endesfelder, D., Einbeck, J., Puig, P., Benadjaoud, M. A., Higueras, M., Ainsbury, E., Gruel, G., Oestreicher, U., Barrios, L., & 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
• A robust Bayesian analysis of variable selection under prior ignorance
  
  Basu, T., Troffaes, M. C., & Einbeck, J. (2023). A robust Bayesian analysis of variable selection under prior ignorance. Sankhya A, 85(1), 1014-1057. https://doi.org/10.1007/s13171-022-00287-2
• Response transformations for random effect and variance component models
  
  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
• The effect of data aggregation on dispersion estimates in count data models
  
  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
• Multisite educational trials: estimating the effect size and its confidence intervals
  
  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
• The impact of a novel medication scanner on administration errors in the hospital setting: a before and after feasibility study
  
  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), Article 86. https://doi.org/10.1186/s12911-022-01828-3
• Using Random Effect Models to Produce Robust Estimates of Death Rates in COVID-19 Data
  
  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
• Effects of maternal mental health on prenatal movement profiles in twins and singletons
  
  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. https://doi.org/10.1111/apa.15903
• The Impact of a Bedside Medication Scanning Device on Administration Errors in the Hospital Setting: A Prospective Observational Study
  
  Tolley, C., Watson, N., Heed, A., Einbeck, J., Medows, S., Wood, L., Campbell, 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
• A graphical tool for assessing the suitability of a count regression model
  
  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
• Effects of maternal mental health on fetal visual preference for face-like compared to non-face like light stimulation
  
  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
• Practical Considerations on Nonparametric Methods for Estimating Intrinsic Dimensions of Nonlinear Data Structures
  
  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
• Prenatal effects of maternal nutritional stress and mental health on the fetal movement profile
  
  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
• Testing fetal abilities: A commentary on studies testing prenatal reactions to light stimulation
  
  Reissland, N., Wood, R., Einbeck, J., & Lane, A. (2020). Testing fetal abilities: A commentary on studies testing prenatal reactions to light stimulation. SSRN. https://doi.org/10.2139/ssrn.3569540
• Improving the accuracy of dose estimates from automatically scored dicentric chromosomes by accounting for chromosome number
  
  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
• Quantile-Based Estimation of the Finite Cauchy Mixture Model
  
  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
• A new and intuitive test for zero modification
  
  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
• A statistical framework for radiation dose estimation with uncertainty quantification from the γ-H2AX assay
  
  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
• k-Boxplots for mixture data
  
  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
• Fisher information under Gaussian quadrature models
  
  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
• Self–consistency–based tests for bivariate distributions
  
  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
• Mixture Models for Prediction from Time Series, with Application to Energy Use Data
  
  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
• Uncertainty of fast biological radiation dose assessment for emergency response scenarios
  
  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., … 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
• The correlation threshold as a strategy for gene filtering, with application to irritable bowel syndrome and breast cancer microarray data
  
  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: An International Journal of Undergraduate Research, 9(2).
• Zero-inflated regression models for radiation-induced chromosome aberration data: A comparative study
  
  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
• A summer with genes: Simple disease classification from microarray data
  
  Einbeck, J., Jackson, S. E., & Kasim, A. (2015). A summer with genes: Simple disease classification from microarray data. Mathematics Today, 51(4), 186-188.
• Some asymptotics for localized principal components and curves
  
  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
• Implementation of a local principal curves algorithm for neutrino interaction reconstruction in a liquid argon volume
  
  Back, J., Barker, G., Boyd, S., Einbeck, J., Haigh, M., Morgan, B., Oakley, B., Ramachers, Y., & Roythorne, D. (2014). Implementation of a local principal curves algorithm for neutrino interaction reconstruction in a liquid argon volume. European Physical Journal C: Particles and Fields, 74(3), Article 2832. https://doi.org/10.1140/epjc/s10052-014-2832-4
• Discussion of ‘Beyond mean regression’
  
  Einbeck, J. (2013). Discussion of ‘Beyond mean regression’. Statistical Modelling, 13(4), 349-354. https://doi.org/10.1177/1471082x13494526
• Validation tests for semi-parametric models
  
  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
• Challenging the curse of dimensionality in multivariate local linear regression
  
  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
• A number-of-modes reference rule for density estimation under multimodality
  
  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
• Goodness-of-fit tests in semi-linear models
  
  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
• Using principal curves to analyse traffic patterns on freeways
  
  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
• Bandwidth Selection for Mean-shift based Unsupervised Learning Techniques: a Unified Approach via Self-coverage
  
  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
• Data compression and regression through local principal curves and surfaces
  
  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
• Weighted Repeated Median Smoothing and Filtering
  
  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
• A new package for fitting random effect models
  
  Einbeck, J., Hinde, J., & Darnell, R. (2007). A new package for fitting random effect models. R News, 7(1), 26-30.
• On design-weighted local fitting and its relation to the Horvitz-Thompson estimator
  
  Einbeck, J., & Augustin, T. (2007). On design-weighted local fitting and its relation to the Horvitz-Thompson estimator. Statistica Sinica, 19(1), 103-123.
• Software for calculating blood lactate endurance markers
  
  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
• Modelling beyond regression functions: An application of multimodal regression to speed-flow data
  
  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
• A note on NPML estimation for exponential family regression models with unspecified dispersion parameter
  
  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.
• Local Principal Curves
  
  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
• Local fitting with a power basis
  
  Einbeck, J. (2004). Local fitting with a power basis. Revstat Statistical Journal, 2(2), 102-126.
• Local Smoothing with Robustness against outlying Predictors
  
  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
• A Simple Unifying Formula for Taylor's Theorem and Cauchy's Mean Value Theorem
  
  Einbeck, J. (2004). A Simple Unifying Formula for Taylor’s Theorem and Cauchy’s Mean Value Theorem. International Journal of Pure and Applied Mathematics : IJPAM., 14(1), 69-74.
• Online Monitoring with Local Smoothing Methods and Adaptive Ridging
  
  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
• Multivariate Local Fitting with General Basis Functions
  
  Einbeck, J. (2003). Multivariate Local Fitting with General Basis Functions. Computational Statistics, 18(2), 185-203. https://doi.org/10.1007/s001800300140

Report

• The differential impact of Covid-19 related school closures on English primary school pupils’ writing performance
  
  Beckmann, J., Einbeck, J., & Hunt, A. (2025). The differential impact of Covid-19 related school closures on English primary school pupils’ writing performance. Nuffield Foundation.
• Evaluating the impact of the Parents and Children Together (PACT) programme on the language skills of 3- to 4-year-old nursery children A two-armed randomised trial
  
  Menzies, V., Eerola, P.-S., Zhang, Q., Cramman, H., & Einbeck, J. (2024). Evaluating the impact of the Parents and Children Together (PACT) programme on the language skills of 3- to 4-year-old nursery children A two-armed randomised trial. Educational Endowment Foundation.
• Improving power calculations in educational trials
  
  Singh, A., Uwimpuhwe, G., Vallis, D., Akhter, N., Coolen-Maturi, T., Einbeck, J., Higgins, S., Culliney, M., & Demack, S. (2023). Improving power calculations in educational trials. Education Endowment Foundation.
• Parents and Children Together (PACT) Evaluation Report
  
  Menzies, V., Cramman, H., Eerola, P., Hugill-Jones, J., Akhter, N., & Einbeck, J. (2022). Parents and Children Together (PACT) Evaluation Report.
• Individual-participant-data-meta-analysis-of-the-impact-of-EEF-trials-on-the-educational-attainment-of-pupils-on-Free-School-Meals
  
  Ashraf, B., Singh, A., Uwimpuhwe, G., Coolen-Maturi, T., Einbeck, J., Higgins, S., & Kasim, A. (2021). Individual-participant-data-meta-analysis-of-the-impact-of-EEF-trials-on-the-educational-attainment-of-pupils-on-Free-School-Meals. EEF.