Rafael Izbicki | PhD
Rafael Izbicki | PhD
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Nonparametric Statistics
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, 2022
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
Code
Conditional density estimation using Fourier series and neural networks
Most machine learning tools aim at creating good predictions for new samples. However, obtaining 100% is not feasible in most problems, …
M. H. de A. Inácio
,
Rafael Izbicki
May, 2018
KDMiLe - Symposium on Knowledge Discovery, Mining and Learning - Algorithms Track
PDF
Converting High-Dimensional Regression to High-Dimensional Conditional Density Estimation
Here we propose a fully nonparametric approach to conditional density estimation that reformulates CDE as a non-parametric orthogonal series problem where the expansion coefficients are estimated by regression. By taking such an approach, one can efficiently estimate conditional densities and not just expectations in high dimensions by drawing upon the success in high-dimensional regression. We show applications to photometric galaxy data, Twitter data, and line-of-sight velocities in a galaxy cluster.
Rafael Izbicki
,
Ann B. Lee
November, 2017
Electronic Journal of Statistics
Preprint
PDF
Code
A unified framework for constructing, tuning and assessing photometric redshift density estimates in a selection bias setting
Photometric redshift estimation is an indispensable tool of precision cosmology. One problem that plagues the use of this tool in the …
P.E. Freeman
,
Rafael Izbicki
,
A.B. Lee
October, 2017
Monthly Notices of the Royal Astronomical Society
Preprint
PDF
Photo-z estimation: An example of nonparametric conditional density estimation under selection bias
We describe a general framework for properly constructing and assessing nonparametric conditional density estimators under selection bias, and for combining two or more estimators for optimal performance. This leads to new improved photo-z estimators. We illustrate our methods on data from the Sloan Data Sky Survey and an application to galaxy-galaxy lensing.
Rafael Izbicki
,
Ann B. Lee
,
Peter E. Freeman
February, 2017
The Annals of Applied Statistics
Preprint
PDF
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