Nonparametric Statistics

Likelihood-Free Frequentist Inference: Bridging Classical Statistics and Machine Learning for Reliable Simulator-Based Inference
N. Dalmasso, L. Masserano, D. Zhao, Rafael Izbicki, A. B. Lee
June, 2024 Electronic Journal of Statistics
Preprint
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
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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
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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
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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
ABC-CDE: Toward Approximate Bayesian Computation with Complex High-Dimensional Data and Limited Simulations