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Rafael Izbicki
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Religion as a protective factor against drug use among Brazilian university students: a national survey
FC Gomes, AG de Andrade, R Izbicki, AM Almeida, LG de Oliveira
Revista Brasileira de Psiquiatria 35 (1), 29-37, 2013
1672013
Depressive morbidity and gender in community-dwelling Brazilian elderly: systematic review and meta-analysis
R Barcelos-Ferreira, R Izbicki, DC Steffens, CM Bottino
Int Psychogeriatr 22 (5), 712-26, 2010
1462010
Screening for frailty with the FRAIL scale: a comparison with the phenotype criteria
I Aprahamian, NO de Castro Cezar, R Izbicki, SM Lin, DLV Paulo, ...
Journal of the American Medical Directors Association 18 (7), 592-596, 2017
1402017
Hypertension and frailty in older adults
I Aprahamian, E Sassaki, MF Dos Santos, R Izbicki, RC Pulgrossi, ...
The Journal of Clinical Hypertension 20 (1), 186-192, 2018
1052018
Evaluation of probabilistic photometric redshift estimation approaches for The Rubin Observatory Legacy Survey of Space and Time (LSST)
SJ Schmidt, AI Malz, JYH Soo, IA Almosallam, M Brescia, S Cavuoti, ...
Monthly Notices of the Royal Astronomical Society 499 (2), 1587-1606, 2020
1022020
New image statistics for detecting disturbed galaxy morphologies at high redshift
PE Freeman, R Izbicki, AB Lee, JA Newman, CJ Conselice, ...
Monthly Notices of the Royal Astronomical Society 434 (1), 282-295, 2013
932013
Converting high-dimensional regression to high-dimensional conditional density estimation
R Izbicki, A B. Lee
922017
A subtest analysis of the Montreal cognitive assessment (MoCA): which subtests can best discriminate between healthy controls, mild cognitive impairment and Alzheimer's disease?
JF Cecato, JE Martinelli, R Izbicki, MS Yassuda, I Aprahamian
International psychogeriatrics 28 (5), 825-832, 2016
872016
Nonparametric conditional density estimation in a high-dimensional regression setting
R Izbicki, AB Lee
Journal of Computational and Graphical Statistics 25 (4), 1297-1316, 2016
722016
Flexible distribution-free conditional predictive bands using density estimators
R Izbicki, GT Shimizu, RB Stern
Proceedings of Machine Learning Research - AISTATS, 3068-3077, 2020
672020
Aprendizado de máquina: uma abordagem estatística
Izbicki, Rafael, TM Santos
http://www.rizbicki.ufscar.br/AME.pdf, 2020
58*2020
Conditional density estimation tools in python and R with applications to photometric redshifts and likelihood-free cosmological inference
N Dalmasso, T Pospisil, AB Lee, R Izbicki, PE Freeman, AI Malz
Astronomy and Computing 30, 100362, 2020
472020
High-dimensional density ratio estimation with extensions to approximate likelihood computation
R Izbicki, A Lee, C Schafer
Artificial intelligence and statistics, 420-429, 2014
402014
ABC–CDE: Toward approximate Bayesian computation with complex high-dimensional data and limited simulations
R Izbicki, AB Lee, T Pospisil
Journal of Computational and Graphical Statistics 28 (3), 481-492, 2019
382019
A spectral series approach to high-dimensional nonparametric regression
AB Lee, R Izbicki
382016
Can the CAMCOG be a good cognitive test for patients with Alzheimer's disease with low levels of education?
I Aprahamian, JE Martinelli, J Cecato, R Izbicki, MS Yassuda
International psychogeriatrics 23 (1), 96-101, 2011
372011
Cd-split and hpd-split: Efficient conformal regions in high dimensions
R Izbicki, G Shimizu, RB Stern
Journal of Machine Learning Research 23 (87), 1-32, 2022
352022
Machine learning for predictive analyses in health: an example of an application to predict death in the elderly in São Paulo, Brazil
HG Santos, CF Nascimento, R Izbicki, YAO Duarte, P Chiavegatto Filho, ...
Cadernos de Saúde Pública 35, e00050818, 2019
34*2019
A unified framework for constructing, tuning and assessing photometric redshift density estimates in a selection bias setting
PE Freeman, R Izbicki, AB Lee
Monthly Notices of the Royal Astronomical Society 468 (4), 4556-4565, 2017
342017
Quantification under prior probability shift: the ratio estimator and its extensions
AF Vaz, R Izbicki, RB Stern
Journal of Machine Learning Research, 2019
332019
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