Rafael Izbicki | PhD
Rafael Izbicki | PhD
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The Latent Dirichlet Allocation model with covariates (LDAcov): a case study on the effect of fire on species composition in Amazonian forests
Understanding and predicting the effect of global change phenomena on biodiversity is challenging given that biodiversity data are …
D. Valle
,
G. Shimizu
,
Rafael Izbicki
,
L. Maracahipes
,
D. Silvério
,
L. Paolucci
,
Y. Jameel
,
P. Brando
February, 2021
Ecology and Evolution
PDF
Distance assessment and analysis of high-dimensional samples using variational autoencoders
An important question in many machine learning applications is whether two samples arise from the same generating distribution. …
M. H. de A. Inácio
,
Rafael Izbicki
,
B. Gyires-Tóth
January, 2021
Information Sciences
Preprint
PDF
How many hospitalizations has the COVID-19 vaccination already prevented in São Paulo?
Rafael Izbicki
,
L. S. Bastos
,
M. Izbicki
,
H. F. Lopes
,
T. M. dos Santos
January, 2021
Clinics
PDF
Sensitivity and specificity in prevalence studies: the importance of considering uncertainty
Rafael Izbicki
,
M. A. Diniz
,
L. S Bastos
October, 2020
Clinics
PDF
Agnostic tests can control the type I and type II errors simultaneously
Despite its common practice, statistical hypothesis testing presents challenges in interpretation. For instance, in the standard frequentist framework there is no control of the type II error. As a result, the non-rejection of the null hypothesis (H0) cannot reasonably be interpreted as its acceptance. We propose that this dilemma can be overcome by using agnostic hypothesis tests, since they can control the type I and II errors simultaneously. In order to make this idea operational, we show how to obtain agnostic hypothesis in typical models. For instance, we show how to build (unbiased) uniformly most powerful agnostic tests and how to obtain agnostic tests from standard p-values. Also, we present conditions such that the above tests can be made logically coherent. Finally, we present examples of consistent agnostic hypothesis tests.
Victor Coscrato
,
Rafael Izbicki
,
Rafael B. Stern
May, 2020
In
BJPS
PDF
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Code
Source Document
DOI
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
The NN-Stacking: Feature weighted linear stacking through neural networks
Stacking methods improve the prediction performance of regression models. A simple way to stack base regressions estimators is by …
V. A. Coscrato
,
M. H. de A. Inácio
,
Rafael Izbicki
January, 2020
Neurocomputing
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
Schrödinger's phenotypes: herbarium specimens show two-dimensional images are both good and (not so) bad sources of morphological data
L. M. Borges
,
V. C. Reis
,
Rafael Izbicki
January, 2020
Methods in Ecology and Evolution
PDF
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