| Nonparametric Bayesian inference from right censored survival data, using the Gibbs sampler E Arjas, D Gasbarra Statistica sinica, 505-524, 1994 | 250 | 1994 |
| Estimation of an errors‐in‐variables regression model when the variances of the measurement errors vary between the observations SB Kulathinal, K Kuulasmaa, D Gasbarra Statistics in medicine 21 (8), 1089-1101, 2002 | 83 | 2002 |
| Gaussian bridges D Gasbarra, T Sottinen, E Valkeila Stochastic Analysis and Applications: The Abel Symposium 2005, 361-382, 2007 | 66 | 2007 |
| Bayesian inference of survival probabilities, under stochastic ordering constraints E Arjas, D Gasbarra Journal of the American Statistical Association 91 (435), 1101-1109, 1996 | 49 | 1996 |
| Enlargement of filtration and additional information in pricing models: Bayesian approach Y Kabanov, R Liptser, J Stoyanov, D Gasbarra, E Valkeila, L Vostrikova From Stochastic Calculus to Mathematical Finance: The Shiryaev Festschrift …, 2006 | 36 | 2006 |
| Estimation of Viterbi path in Bayesian hidden Markov models J Lember, D Gasbarra, A Koloydenko, K Kuljus Metron 77, 137-169, 2019 | 32 | 2019 |
| Testing equality of cause-specific hazard rates corresponding to m competing risks among K groups SB Kulathinal, D Gasbarra Lifetime Data Analysis 8, 147-161, 2002 | 32 | 2002 |
| Backward simulation of ancestors of sampled individuals D Gasbarra, MJ Sillanpää, E Arjas Theoretical Population Biology 67 (2), 75-83, 2005 | 29 | 2005 |
| A new framework for MR diffusion tensor distribution KN Magdoom, S Pajevic, G Dario, PJ Basser Scientific Reports 11 (1), 2766, 2021 | 28 | 2021 |
| Conditional full support of Gaussian processes with stationary increments D Gasbarra, T Sottinen, H Van Zanten Journal of Applied Probability 48 (2), 561-568, 2011 | 26 | 2011 |
| Estimating genealogies from unlinked marker data: a Bayesian approach D Gasbarra, M Pirinen, MJ Sillanpää, E Salmela, E Arjas Theoretical population biology 72 (3), 305-322, 2007 | 26 | 2007 |
| Optimal designs to select individuals for genotyping conditional on observed binary or survival outcomes and non-genetic covariates J Karvanen, S Kulathinal, D Gasbarra Computational Statistics & Data Analysis 53 (5), 1782-1793, 2009 | 22 | 2009 |
| Analysis of competing risks by using Bayesian smoothing D Gasbarra, SR Karia Scandinavian Journal of Statistics 27 (4), 605-617, 2000 | 22 | 2000 |
| Estimating haplotype frequencies by combining data from large DNA pools with database information D Gasbarra, S Kulathinal, M Pirinen, MJ Sillanpaa IEEE/ACM Transactions on Computational Biology and Bioinformatics 8 (1), 36-44, 2009 | 20 | 2009 |
| Estimating population haplotype frequencies from pooled DNA samples using PHASE algorithm M Pirinen, S Kulathinal, D Gasbarra, MJ Sillanpää Genetics Research 90 (6), 509-524, 2008 | 15 | 2008 |
| Estimating genealogies from linked marker data: a Bayesian approach D Gasbarra, M Pirinen, MJ Sillanpää, E Arjas BMC bioinformatics 8, 1-31, 2007 | 15 | 2007 |
| Comment on “On the Metropolis-Hastings acceptance probability to add or drop a quantitative trait locus in Markov chain Monte Carlo-based Bayesian analyses” MJ Sillanpää, D Gasbarra, E Arjas Genetics 167 (2), 1037-1037, 2004 | 15 | 2004 |
| On prequential model assessment in life history analysis E Arjas, D Gasbarra Biometrika, 505-522, 1997 | 14 | 1997 |
| Joint modelling of recurrent infections and antibody response by Bayesian data augmentation M Eerola, D Gasbarra, P Helena Mäkelä, H Linden, A Andreev Scandinavian journal of statistics 30 (4), 677-698, 2003 | 11 | 2003 |
| A novel framework for in-vivo diffusion tensor distribution MRI of the human brain KN Magdoom, AV Avram, JE Sarlls, G Dario, PJ Basser NeuroImage 271, 120003, 2023 | 10 | 2023 |
