An overview of robust Bayesian analysis JO Berger, E Moreno, LR Pericchi, MJ Bayarri, JM Bernardo, JA Cano, ... Test 3 (1), 5-124, 1994 | 787 | 1994 |
Addressing voice recording replications for Parkinson’s disease detection L Naranjo, CJ Perez, Y Campos-Roca, J Martin Expert Systems with Applications 46, 286-292, 2016 | 148 | 2016 |
A two-stage variable selection and classification approach for Parkinson’s disease detection by using voice recording replications L Naranjo, CJ Perez, J Martin, Y Campos-Roca Computer methods and programs in biomedicine 142, 147-156, 2017 | 106 | 2017 |
Simulación: métodos y aplicaciones DR Insua, SR Insua, JM Jiménez Ra-ma, 1997 | 104 | 1997 |
Simulación: métodos y aplicaciones D Ríos Insua, JM Jiménez Madrid: Ra-Ma,, 2009 | 62* | 2009 |
Bayesian analysis of a generalized lognormal distribution J Martín, CJ Pérez Computational Statistics & Data Analysis 53 (4), 1377-1387, 2009 | 48 | 2009 |
Computer-aided diagnosis system: A Bayesian hybrid classification method F Calle-Alonso, CJ Pérez, JP Arias-Nicolás, J Martín Computer methods and programs in biomedicine 112 (1), 104-113, 2013 | 46 | 2013 |
Robust bayesian analysis F Ruggeri, DR Insua, J Martín Handbook of statistics 25, 623-667, 2005 | 44 | 2005 |
Log-linear pool to combine prior distributions: A suggestion for a calibration-based approach MJ Rufo, J Martín, CJ Pérez | 40 | 2012 |
Misclassified multinomial data: a Bayesian approach CJ Pérez, FJ Girón, J Martín, M Ruiz, C Rojano RACSAM 101 (1), 71-80, 2007 | 34 | 2007 |
Logistic regression for simulating damage occurrence on a fruit grading line C Bielza, P Barreiro, MI Rodrıguez-Galiano, J Martın Computers and electronics in agriculture 39 (2), 95-113, 2003 | 31 | 2003 |
Bayesian forecasting for accident proneness evaluation SR Insua, J Martin, DR Insua, F Ruggeri Scandinavian Actuarial Journal 1999 (2), 134-156, 1999 | 31 | 1999 |
Issues in Bayesian loss robustness J Martín, DR Insua, F Ruggeri Sankhyā: The Indian Journal of Statistics, Series A, 405-417, 1998 | 30 | 1998 |
Diagnosis and tracking of Parkinson’s disease by using automatically extracted acoustic features C Perez, Y Campos-Roca, L Naranjo, J Martin J Alzheimers Dis Parkinsonism 6 (260), 2161-0460, 2016 | 29 | 2016 |
Non-parametric Bayesian estimation for multitype branching processes through simulation-based methods M González, J Martin, R Martinez, M Mota Computational Statistics & Data Analysis 52 (3), 1281-1291, 2008 | 29 | 2008 |
Optimal actions in problems with convex loss functions JP Arias-Nicolás, J Martín, F Ruggeri, A Suárez-Llorens International journal of approximate reasoning 50 (2), 303-314, 2009 | 28 | 2009 |
MCMC-based local parametric sensitivity estimations CJ Pérez, J Martín, MJ Rufo Computational Statistics & Data Analysis 51 (2), 823-835, 2006 | 27 | 2006 |
Addressing voice recording replications for tracking Parkinson’s disease progression L Naranjo, CJ Pérez, J Martín Medical & biological engineering & computing 55, 365-373, 2017 | 26 | 2017 |
Bayesian sensitivity analysis: a review D Rios Insua, F Ruggeri, J Martin Handbook on sensitivity analysis,(A. Saltelli et al. eds.). New York: Wiley, 2000 | 26* | 2000 |
Sensitivity estimations for Bayesian inference models solved by MCMC methods CJ Pérez, J Martín, MJ Rufo Reliability Engineering & System Safety 91 (10-11), 1310-1314, 2006 | 25 | 2006 |