Rafael Izbicki (UFSCar) | PhD
Rafael Izbicki (UFSCar) | PhD
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Nonparametric Statistics
Distribution-Free Conformal Joint Prediction Regions for Neural Marked Temporal Point Processes
V. Dheur
,
T. Bosser
,
Rafael Izbicki
,
S. Ben Taieb
February, 2024
Machine Learning
PDF
Regression Trees for Fast and Adaptive Prediction Intervals
L. M. C. Cabezas
,
M. P. Otto
,
Rafael Izbicki
,
R. B. Stern
February, 2024
Information Sciences
Preprint
PDF
Flexible conditional density estimation for time series
G. Grivol
,
Rafael Izbicki
,
A. A. Okuno
,
R. B. Stern
January, 2024
Brazilian Journal of Probability and Statistics
Preprint
The Quasar Catalogue for S-PLUS DR4 (QuCatS) and the estimation of photometric redshifts
L. Nakazono
,
R. R. Valença
,
G. Soares
,
Rafael Izbicki
,
Ž. Ivezić
January, 2024
Monthly Notices of the Royal Astronomical Society
PDF
Detecting Distributional Differences in Labeled Sequence Data with Application to Tropical Cyclone Satellite Imagery
T. McNeely
,
G. Vincente
,
K. M. Wood
,
Rafael Izbicki
,
A. B. Lee
August, 2023
Annals of Applied Statistics
Preprint
PDF
Conditional Density Estimation Tools in Python and R with Applications to Photometric Redshifts and Likelihood-Free Cosmological Inference
N. Dalmasso
,
T. Pospisil
,
A. B. Lee
,
Rafael Izbicki
,
P. E. Freeman
,
A. I. Malz
January, 2020
Astronomy and Computing
Preprint
PDF
WIKS: A general Bayesian nonparametric index for quantifying differences between two populations
A key problem in many research investigations is to decide whether two samples have the same distribution. Numerous statistical methods …
R. de C. Ceregatti
,
Rafael Izbicki
,
L. E. B. Salasar
January, 2020
Test
PDF
Evaluation of probabilistic photometric redshift estimation approaches for The Rubin Observatory Legacy Survey of Space and Time (LSST)
S. Schmidt
,
A. Malz
,
et al.
,
Rafael Izbicki
January, 2020
Monthly Notices of the Royal Astronomical Society
Preprint
PDF
ABC-CDE: Toward Approximate Bayesian Computation with Complex High-Dimensional Data and Limited Simulations
We show how a nonparametric conditional density estimation (CDE) framework helps address three nontrivial challenges in ABC. (i) how to efficiently estimate the posterior distribution with limited simulations and different types of data, (ii) how to tune and compare the performance of ABC and related methods in estimating the posterior itself, rather than just certain properties of the density, and (iii) how to efficiently choose among a large set of summary statistics based on a CDE surrogate loss.
Rafael Izbicki
,
Taylor Pospisil
,
Ann B. Lee
February, 2019
Journal of Computational and Graphical Statistics
Preprint
PDF
Comparing two populations using Bayesian Fourier series density estimation
M. H. de A. Inácio
,
Rafael Izbicki
,
L. E. B. Salasar
July, 2018
Communications in Statistics – Simulation and Computation.
PDF
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