Publications and communications of Philippe Lambert

Articles accepted in reviewed journal

Lambert, P., & Gressani, O. (October 2023). Penalty parameter selection and asymmetry corrections to Laplace approximations in Bayesian P-splines models. Statistical Modelling, 23 (5-6), 409 - 423. doi:10.1177/1471082X231181173

Kreyenfeld, M., Konietzka, D., Lambert, P., & Ramos, V. J. (02 March 2023). Second Birth Fertility in Germany: Social Class, Gender, and the Role of Economic Uncertainty. European Journal of Population, 39 (1), 5. doi:10.1007/s10680-023-09656-5

Lambert, P. (September 2021). Fast Bayesian inference using Laplace approximations in nonparametric double additive location-scale models with right- and interval-censored data. Computational Statistics and Data Analysis, 161, 107250. doi:10.1016/j.csda.2021.107250

Gressani, O., & Lambert, P. (2021). Laplace approximations for fast Bayesian inference in generalized additive models based on P-splines. Computational Statistics and Data Analysis. doi:10.1016/j.csda.2020.107088

Lambert, P., & Bremhorst, V. (2020). Inclusion of time-varying covariates in cure survival models with an application in fertility studies. Journal of the Royal Statistical Society. Series A, Statistics in Society, 183, 333-354. doi:10.1111/rssa.12501

Bremhorst, V., Kreyenfeld, M., & Lambert, P. (2019). Nonparametric double additive cure survival models: An application to the estimation of the non-linear effect of age at first parenthood on fertility progression. Statistical Modelling, 19 (3), 248-275. doi:10.1177/1471082X18784685

Lambert, P., & Bremhorst, V. (2019). Estimation and identification issues in the promotion time cure model when the same covariates influence long- and short-term survival. Biometrical Journal, 61 (2), 275-289. doi:10.1002/bimj.201700250

Gressani, O., & Lambert, P. (2018). Fast Bayesian inference using Laplace approximations in a flexible promotion time cure model based on P-splines. Computational Statistics and Data Analysis, 124, 151-167. doi:10.1016/j.csda.2018.02.007

Bremhorst, V., Kreyenfeld, M., & Lambert, P. (2016). Fertility progression in Germany: an analysis using flexible nonparametric cure survival models. Demographic Research. doi:10.4054/DemRes.2016.35.18

Bremhorst, V., & Lambert, P. (2016). Flexible estimation in cure survival models using Bayesian P-splines. Computational Statistics and Data Analysis, 93, 270–284. doi:10.1016/j.csda.2014.05.009

Cetinyürek, A., & Lambert, P. (2016). Semi-parametric frailty model for clustered interval-censored data. Statistical Modelling, 16 (5), 360-391. doi:10.1177/1471082X16655631

Frasso, G., Jaeger, J., & Lambert, P. (2016). Inference in dynamic systems using B-splines and quasilinearized ODE penalties. Biometrical Journal, 58 (3), 691-714. doi:10.1002/bimj.201500082

Frasso, G., & Lambert, P. (2016). Bayesian inference in an extended SEIR model with nonparametric disease transmission rate: An application to the Ebola epidemic in Sierra Leone. Biostatistics, 17 (4), 779-792. doi:10.1093/biostatistics/kxw027

Jaspers, S., Lambert, P., & Aerts, M. (2016). A Bayesian approach to the semiparametric estimation of a minimum inhibitory concentration distribution. Annals of Applied Statistics, 10 (2), 906-924. doi:10.1214/16-AOAS918

Frasso, G., Jeager, J., & Lambert, P. (2015). Parameter estimation and inference in dynamic systems described by linear partial differential equations. AStA Advances in Statistical Analysis, 1-29. doi:10.1007/s10182-015-0257-5

Lambert, P. (March 2014). Spline approximation to conditional Archimedean copula. Stat, 3 (1), 200-217. doi:10.1002/sta4.55

Lebrun, P., Boulanger, B., Debrus, B., Lambert, P., & Hubert, P. (2013). A Bayesian Design Space for analytical methods based on multivariate models and predictions. Journal of Biopharmaceutical Statistics, 23, 1330–1351. doi:10.1080/10543406.2013.834922

Lambert, P., & Eilers, P. H. C. (2009). Bayesian density estimation from grouped continuous data. Computational Statistics and Data Analysis, 53, 1388-1399. doi:10.1016/j.csda.2008.11.022

Lambert, P. (15 August 2007). Archimedean copula estimation using Bayesian splines smoothing techniques. Computational Statistics and Data Analysis, 51 (12), 6307-6320. doi:10.1016/j.csda.2007.01.018

Bolancé, C., Denuit, M., Guillén, M., & Lambert, P. (2007). Greatest accuracy credibility with dynamic heterogeneity: the Harvey-Fernandes model. Belgian Actuarial Bulletin, 7 (1), 14-18.

Jullion, A., & Lambert, P. (2007). Robust specification of the roughness penalty prior distribution in spatially adaptive Bayesian P-splines models. Computational Statistics and Data Analysis, 51, 2542-2558. doi:10.1016/j.csda.2006.09.027

Denuit, M., & Lambert, P. (2005). Constraints on concordance measures in bivariate discrete data. Journal of Multivariate Analysis, 93, 40-57. doi:10.1016/j.jmva.2004.01.004

Vandenhende, F., & Lambert, P. (2005). Local dependence estimation using semi-parametric Archimedean copulas. Canadian Journal of Statistics, 33, 377-388. doi:10.1002/cjs.5540330305

Lambert, P., Collett, D., Kimber, A., & Johnson, R. (2004). Parametric accelerated failure time models with random effects and an application to kidney transplant survival. Statistics in Medicine, 23, 3177-3192. doi:10.1002/sim.1876

Cebrian, A., Denuit, M., & Lambert, P. (2003). Analysis of bivariate tail dependence using extreme values copulas: An application to the SOA medical large claims database. Belgian Actuarial Bulletin.

Lambert, P., & Vandenhende, F. (2002). A copula based model for multivariate non normal longitudinal data: analysis of a dose titration safety study on a new antidepressant. Statistics in Medicine, 21, 3197-3217. doi:10.1002/sim.1249

Vandenhende, F., & Lambert, P. (2002). On the joint analysis of longitudinal responses and early discontinuation in randomized trials. Journal of Biopharmaceutical Statistics, 12, 425-440. doi:10.1081/BIP-120016228

Genicot, B., Lambert, P., Votion, D., Close, R., Lindsey, J. K., & Lekeux, P. (1995). Ability of Bronchodilators to Prevent Bovine Experimental Respiratory Distress. Veterinary Research, 26 (4), 276-283.