41. Padilla, O. and De la Cruz, R. (2025+). Bayesian split-population models for estimating recidivism. Chilean Journal of Statistics. In Press.
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40. Hernández, H., Ochoa-Rosales, C., Ibáñez, A., Oyanedel, L., Olavarría, L., Marín-Díaz, N., Caviedes, A.A., Hazelton, J., Ramos Franco, T., Santamaria-García, H., Custodio, N., Montecinos, R., Bruno, M., Avila-Funes, J.A., Matallana, D.L., De la Cruz, R., Petermann-Rocha, F., Slachevsky, A., Duran-Aniotz, C., and González, C. (2025). Chronic Pain in the Chilean population: Risk factors prevalence and cognitive associations. Frontiers in Aging. 6, 1548667.
39. Alvares, D., Meza, C. and De la Cruz, R. (2025). Bayesian inference for nonlinear mixed-effects location scale and interval-censoring cure-survival models: An application to pregnancy miscarriage. Statistical Methods in Medical Research.
38. De la Cruz, R., Lavielle, M., Meza, C., and Núñez-Antón, V. (2024). A joint analysis proposal of nonlinear longitudinal and time-to-event right-, interval-censored data for modeling pregnancy miscarriage. Computers in Biology and Medicine. 18, 109186.
37. Ruiz, E., Yushimito, W. F., Aburto, L. and de la Cruz, R. (2024). Predicting passenger satisfaction in public transportation using machine learning models. Transportation Research Part A: Policy and Practice, 181, 103995.
36. Márquez, M., Meza, C., Lee, D.-J. and De la Cruz, R. (2023). Classification of longitudinal profiles using semi-parametric nonlinear mixed models with P-Splines and the SAEM algorithm. Statistics in Medicine, 42(27), 4952–4971.
35. Blanco, K., Salcidua, S., Orellana, P., Sauma, T., León, T., López-Steinmetz, L.C., Ibáñez, A., Duran-Aniotz, C. and De la Cruz, R. (2023). Systematic review: fluid biomarkers and machine learning methods to improve the diagnosis from mild cognitive impairment to Alzheimer's disease. Alzheimer's Research & Therapy. 15, 176.
34. Gaskins, J.T., Fuentes, C., and De la Cruz, R. (2023). A Bayesian nonparametric model for classification of longitudinal profiles. Biostatistics, 24(1), 209-225.
33. De la Cruz, R., Fuentes, C. and Padilla, O. (2023). A Bayesian mixture cure rate model for estimating short-term and long-term recidivism. Entropy, 25(1), 56.
32. De la Cruz, R., Meza, C., Narria, N., and Fuentes, C. (2022). A Bayesian Change Point Analysis of the USD/CLP Series in Chile from 2018 to 2020: Understanding the Impact of Social Protests and the COVID-19 Pandemic. Mathematics, 10(18), 3380.
31. Aranis, A., De la Cruz, R., Montenegro, C., Ramírez, M., Caballero, L., Gómez, A., and Walker, K. (2022). Meta-estimation of Araucanian herring, Strangomera bentincki (Norman, 1936), biological indicators in the central-south zone of Chile (32º-47º LS). Frontiers in Marine Sciences, 9:886321.
30. De la Cruz, R., Salinas, H. S., and Meza, C. (2022). Reliability Estimation for Stress-Strength Model Based on Unit-Half-Normal Distribution. Symmetry, 14(4), 837.
29. Celis, P., De la Cruz, R., Fuentes, C., and Gómez, H. W. (2021). Survival and reliability analysis with an Epsilon-Positive family of distributions with applications. Symmetry, 13(5), 908.
28. De la Cruz, R., Padilla, O., Valle, M.A., and Ruz, G.A. (2021). Modeling recidivism through Bayesian regression models and deep neural networks. Mathematics, 9(6), 639.
27. De la Cruz, R., Fuentes, C., Meza, C., and Núñez-Antón, V. (2018). Error rate estimation in discriminant analysis for non-linear longitudinal data: a comparison of resampling methods. Statistical Methods in Medical Research, 27(4), 1153-1167.
26. De la Cruz, R., Fuentes, C., Meza, C., Lee, D.-J., and Arribas-Gil, A. (2017). Predicting pregnancy outcomes using longitudinal information: a penalized splines mixed-effects model approach. Statistics in Medicine, 36, 2120-2134.
25. De la Cruz, R., Arribas-Gil, A., Meza, C., and Carroll, R.J. (2016). Bayesian regression analysis of data with random effects covariates from nonlinear longitudinal measurements. Journal of Multivariate Analysis, 143, 94-106.
24. Bedregal, P., Hernández, V., Mingo, V.M., Castañón, C., Valenzuela, P. Moore, R., de la Cruz, R., and Castro, D. (2016). Early child development inequalities and associated factors between public and private providers at metropolitan region in Chile. Revista Chilena de Pediatría, 87, 351-358.
23. Ferreccio, C., Roa, J.C., Bambs, C., Vives, A., Corvalán, A.H., Cortés, S., Foerster, C., Acevedo, J., Huidobro, A., Passi, A., Toro, P., Covacevich, Y., De la Cruz, R., Koshiol, J., Olivares, M., Miquel, J.F., Cruz, F., Silva, R., Quest, A.F., Kogan, M.J., Castro, P.F., and Lavandero, S. (2016). Study protocol for the Maule Cohort (MAUCO) of chronic diseases, Chile 2014-2024. BMC Public Health, 16(1), 122.
22. Arribas-Gil, A. De la Cruz, R., Meza, C., and Lebarbier, E. (2015). Classification of longitudinal data through a semiparametric mixed-effects models based on Lasso-type estimators. Biometrics, 71(2), 333-343.
21. De la Cruz, R. (2014). Bayesian analysis for nonlinear mixed-effects models under heavy-tailed distributions. Pharmaceutical Statistics, 13, 81-93. Special Issue on Bayesian Methods in Drug Development and Regulatory Reviews.
20. Rabagliati, R., Bertín , P., Cerón, I., Rojas, H., Domínguez, I., Vera, A., Siri, L., Flores, J., Fernández, P., Pérez, M., and de la Cruz, R. (2014). Epidemiology of febrile neutropenia in adult patients with acute leukemia and lymphoma. Cohort study of public and private hospital of Santiago, Chile. Revista Chilena de Infectología, 31(6), 721-728.
19. Salinas, E.A., De la Cruz, R., and Bastías, G. (2014). Nonattendance to medical specialists appointments and its relation to regional environmental and socioeconomic indicators in the Chilean public health system. Medwave, 14(9), e6023.
18. De la Cruz, R. (2012). Review of the book "Bayesian Ideas and Data Analysis: An Introduction for Scientists and Statisticians" by R. Christensen, W. Johnson, A. Branscum and T.E. Hanson. Journal of Applied Statistics, 39(10), 2306-2307.
17. Meza, C., Osorio, F., and De la Cruz, R. (2012). Estimation in nonlinear mixed-effects models using heavy-tailed distributions. Statistics and Computing, 22(1), 121-139.
16. Fuentes, R., Maturana, M., and De la Cruz, R. (2012). Efficacy of nifurtimox for the treatment of chronic Chagas disease. Revista Chilena de Infectología, 29, 82-86.
15. De la Cruz, R., Marshall, G., and Quintana, F.A. (2011). Logistic regression when covariates are random effects from a non-linear mixed model. Biometrical Journal, 53(5), 735-749.
14. Pewsey, A., Shimizu, K., and De la Cruz, R. (2011). On an extension of the von Mises distribution due to Batschelet. Journal of Applied Statistics, 38(5), 1073-1085.
13. Santos, J.L., De la Cruz, R., Holst, C., Grau, K., Naranjo, C., Maiz, A., Astrup, A., et al. (2011). Allelic variants of the melanocortin-3 receptor gene (MC3R) and weight loss in obesity: a randomised trial of hypo-energetic high- versus low-fat diets. PLoS ONE 6(6):e19934.
12. Bedregal, P., Hernández, V., Prado, P., Castañon, C., Mingo, V., and de la Cruz, R. (2010). Hacia la evaluación de "Chile Crece Contigo": Resultados psicosociales del estudio piloto. Revista Médica de Chile, 138(6), 770-772.
11. Valderas, J.P., Irribarra, V., Boza, C., De la Cruz, R., Liberona, Y., Acosta, A.M., Yolito, M., and Maiz, A. (2010). Medical and surgical treatments for obesity have opposite effects on peptide YY and appetite: a prospective study controlled for weight loss. Journal of Clinical Endocrinology & Metabolism, 95(3), 1069-1075.
10. De la Cruz, R., and Branco, M. (2009). Bayesian analysis for nonlinear regression model under skewed errors, with application in growth curves. Biometrical Journal, 51(4), 588-609.
9. Marshall, G., De la Cruz-Mesía, R., Quintana, F. A., and Barón A. E. (2009). Discriminant analysis for multivariate longitudinal markers with possibly missing data. Biometrics, 65(1), 69-80.
8. De la Cruz, R. (2008). Bayesian nonlinear regression models with skew-elliptical errors: Applications to the classification of longitudinal profiles. Computational Statistics and Data Analysis, 53(2), 436-449.
7. De la Cruz-Mesía, R., Quintana, F.A., and Marshall, G. (2008). Model-based clustering for longitudinal data. Computational Statistics and Data Analysis, 52(3), 1441-1457.
6. De la Cruz-Mesía, R. (2007). Review of the book "Introduction to Randomized Controlled Clinical Trials, 2nd Edition" by J.N.S. Matthews. Journal of Applied Statistics, 34(8), 1012-1013.
5. De la Cruz-Mesía, R., and Quintana, F.A. (2007). A model-based approach to Bayesian classification with applications to predicting pregnancy outcomes from longitudinal beta-hCG profiles. Biostatistics, 8(2), 228-238.
4. De la Cruz-Mesía, R., Quintana, F.A., and Müller, P. (2007). Semiparametric Bayesian classification with longitudinal markers. Journal of the Royal Statistical Society, Series C, (Applied Statistics), 56(2), 119-137.
3. Marshall, G., De la Cruz-Mesía, R., Barón, A., Rutledge, J.H., and Zerbe, G. (2006). Nonlinear random effects models for multivariate responses with missing data. Statistics in Medicine, 25(16), 2817-2830.
2. De la Cruz-Mesía, R., and Marshall, G. (2006). Nonlinear random effects models with continuous time autoregressive errors: A Bayesian approach. Statistics in Medicine, 25 (9), 1471-1484.
1. De la Cruz-Mesía, R., and Marshall, G. (2003). A Bayesian approach for nonlinear regression model with continuous errors. Communications in Statistics: Theory and Methods, 32 (8), 1631-1646.
Work Submitted to Publication and Still Under Review
De la Cruz, R., Lavielle, M., Meza, C., and Núñez-Antón, V. Alternative subgroup joint analysis proposal of nonlinear longitudinal and time-to-event data for modeling pregnancy miscarriage.
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Román, J.C., Humeniy, N., and De la Cruz, R. Geometric ergodicity of gibbs samplers for Bayesian general linear mixed models with t-distributed effects.
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