Villa, C; Rubio, FJ; (2018) Objective priors for the number of degrees of freedom of a multivariate t distribution and the t-copula. Computational statistics & data analysis, 124. pp. 197-219. ISSN 0167-9473 DOI: https://doi.org/10.1016/j.csda.2018.03.010

Ghebremichael-Weldeselassie, Y; Whitaker, HJ; Douglas, IJ; Smeeth, L; Farrington, CP; (2017) Self-controlled case series with multiple event types. Computational statistics & data analysis, 113. pp. 64-72. ISSN 0167-9473 DOI: https://doi.org/10.1016/j.csda.2016.10.010 Full text not available from this repository.

Lee, D; Lee, Y; Paik, MC; Kenward, MG; (2013) Robust inference using hierarchical likelihood approach for heavy-tailed longitudinal outcomes with missing data: An alternative to inverse probability weighted generalized estimating equations. Computational statistics & data analysis, 59. pp. 171-179. ISSN 0167-9473 DOI: https://doi.org/10.1016/j.csda.2012.10.013 Full text not available from this repository.

Pantazis, N; Kenward, MG; Touloumi, G; Eurocoord, CascadeCollaboration; (2013) Performance of parametric survival models under non-random interval censoring: A simulation study. Computational statistics & data analysis, 63. pp. 16-30. ISSN 0167-9473 DOI: https://doi.org/10.1016/j.csda.2013.01.014 Full text not available from this repository.

Daniel, RM; Kenward, MG; (2012) A method for increasing the robustness of multiple imputation. Computational statistics & data analysis, 56 (6). pp. 1624-1643. ISSN 0167-9473 DOI: https://doi.org/10.1016/j.csda.2011.10.006

Sotto, C; Beunckens, C; Molenberghs, G; Kenward, MG; (2011) MCMC-based estimation methods for continuous longitudinal data with non-random (non)-monotone missingness. Computational statistics & data analysis, 55 (1). pp. 301-311. ISSN 0167-9473 DOI: https://doi.org/10.1016/j.csda.2010.04.026 Full text not available from this repository.

Molenberghs, G; Kenward, MG; (2010) Semi-parametric marginal models for hierarchical data and their corresponding full models. Computational statistics & data analysis, 54 (2). pp. 585-597. ISSN 0167-9473 DOI: https://doi.org/10.1016/j.csda.2009.09.040 Full text not available from this repository.

White, IR; Daniel, R; Royston, P; (2010) Avoiding bias due to perfect prediction in multiple imputation of incomplete categorical variables. Computational statistics & data analysis, 54 (10). pp. 2267-2275. ISSN 0167-9473 DOI: https://doi.org/10.1016/j.csda.2010.04.005 Full text not available from this repository.

Kenward, MG; Roger, JH; (2009) An improved approximation to the precision of fixed effects from restricted maximum likelihood. Computational statistics & data analysis, 53 (7). pp. 2583-2595. ISSN 0167-9473 DOI: https://doi.org/10.1016/j.csda.2008.12.013 Full text not available from this repository.

Sepulveda, N; Paulino, CD; Penha-Goncalves, C; (2009) Bayesian analysis of allelic penetrance models for complex binary traits. Computational statistics & data analysis, 53 (4). pp. 1271-1283. ISSN 0167-9473 https://researchonline.lshtm.ac.uk/id/eprint/1086 Full text not available from this repository.

Webb, EL; Forster, JJ; (2008) Bayesian model determination for multivariate ordinal and binary data. Computational statistics & data analysis, 52 (5). pp. 2632-2649. ISSN 0167-9473 DOI: https://doi.org/10.1016/j.csda.2007.09.008 Full text not available from this repository.

Jansen, I; Hens, N; Molenberghs, G; Aerts, M; Verbeke, G; Kenward, MG; (2006) The nature of sensitivity in monotone missing not at random models. Computational statistics & data analysis, 50 (3). pp. 830-858. ISSN 0167-9473 DOI: https://doi.org/10.1016/j.csda.2004.10.009 Full text not available from this repository.

Molenberghs, G; Verbeke, C; Thijs, H; Lesaffre, E; Kenward, MG; (2001) Influence analysis to assess sensitivity of the dropout process. Computational statistics & data analysis, 37 (1). pp. 93-113. ISSN 0167-9473 https://researchonline.lshtm.ac.uk/id/eprint/17415 Full text not available from this repository.