| Uplift random forests L Guelman, M Guillén, AM Pérez-Marín Cybernetics and Systems 46 (3-4), 230-248, 2015 | 127 | 2015 |
| The use of telematics devices to improve automobile insurance rates M Guillen, JP Nielsen, M Ayuso, AM Pérez‐Marín Risk analysis 39 (3), 662-672, 2019 | 121 | 2019 |
| Time-varying effects in the analysis of customer loyalty: A case study in insurance M Guillén, JP Nielsen, TH Scheike, AM Pérez-Marín Expert systems with Applications 39 (3), 3551-3558, 2012 | 115 | 2012 |
| Telematics and gender discrimination: Some usage-based evidence on whether men’s risk of accidents differs from women’s M Ayuso, M Guillen, AM Pérez-Marín Risks 4 (2), 10, 2016 | 101 | 2016 |
| Time and distance to first accident and driving patterns of young drivers with pay-as-you-drive insurance M Ayuso, M Guillén, AM Pérez-Marín Accident Analysis & Prevention 73, 125-131, 2014 | 95 | 2014 |
| Random forests for uplift modeling: an insurance customer retention case L Guelman, M Guillén, AM Pérez-Marín International conference on modeling and simulation in engineering …, 2012 | 95 | 2012 |
| The need to monitor customer loyalty and business risk in the European insurance industry M Guillen, JP Nielsen, AM Pérez-Marín The Geneva Papers on Risk and Insurance-Issues and Practice 33, 207-218, 2008 | 84 | 2008 |
| Using GPS data to analyse the distance travelled to the first accident at fault in pay-as-you-drive insurance M Ayuso, M Guillen, AMP Marín Transportation research part C: emerging technologies 68, 160-167, 2016 | 82 | 2016 |
| Pay-as-you-drive insurance: the effect of the kilometers on the risk of accident JP Boucher, AM Pérez-Marín, M Santolino Anales del Instituto de Actuarios Españoles 19 (3), 135-154, 2013 | 75 | 2013 |
| Survival analysis of a household portfolio of insurance policies: how much time do you have to stop total customer defection? PL Brockett, LL Golden, M Guillen, JP Nielsen, J Parner, AM Perez‐Marin Journal of Risk and Insurance 75 (3), 713-737, 2008 | 75 | 2008 |
| Can automobile insurance telematics predict the risk of near-miss events? M Guillen, JP Nielsen, AM Pérez-Marín, V Elpidorou North American Actuarial Journal 24 (1), 141-152, 2020 | 65 | 2020 |
| Recent mortality trends in the Spanish population A Felipe, M Guillén, AM Perez-Marin British Actuarial Journal 8 (4), 757-786, 2002 | 65 | 2002 |
| Near‐miss telematics in motor insurance M Guillen, JP Nielsen, AM Pérez‐Marín Journal of Risk and Insurance 88 (3), 569-589, 2021 | 59 | 2021 |
| A decision support framework to implement optimal personalized marketing interventions L Guelman, M Guillén, AM Pérez-Marín Decision Support Systems 72, 24-32, 2015 | 55 | 2015 |
| A survey of personalized treatment models for pricing strategies in insurance L Guelman, M Guillén, AM Perez-Marin Insurance: Mathematics and Economics 58, 68-76, 2014 | 52 | 2014 |
| Performance measurement of pension strategies: A case study of Danish life-cycle products M Guillén, JP Nielsen, AM Pérez-Marín, KS Petersen Scandinavian Actuarial Journal 2013 (1), 49-68, 2013 | 37 | 2013 |
| Semi-autonomous vehicles: Usage-based data evidences of what could be expected from eliminating speed limit violations AM Pérez-Marín, M Guillen Accident Analysis & Prevention 123, 99-106, 2019 | 36 | 2019 |
| Optimal personalized treatment rules for marketing interventions: A review of methods, a new proposal, and an insurance case study L Guelman, M Guillén, AM Pérez Marín UB Riskcenter Working Paper Series, 2014/06, 2014 | 35 | 2014 |
| Assessing driving risk using internet of vehicles data: An analysis based on generalized linear models S Sun, J Bi, M Guillen, AM Pérez-Marín Sensors 20 (9), 2712, 2020 | 34 | 2020 |
| Quantile regression with telematics information to assess the risk of driving above the posted speed limit AM Pérez-Marín, M Guillen, M Alcañiz, L Bermúdez Risks 7 (3), 80, 2019 | 26 | 2019 |
