Výsledky vyhledávání - "Alexandre G. Patriota"

Self-consistent confidence sets and tests of composite hypotheses applicable to restricted parameters

Autor: Alexandre G. Patriota, David R. Bickel

Publikováno v: Bernoulli 25, no. 1 (2019), 47-74
Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual)
Universidade de São Paulo (USP)
instacron:USP

Frequentist methods, without the coherence guarantees of fully Bayesian methods, are known to yield self-contradictory inferences in certain settings. The framework introduced in this paper provides a simple adjustment to $p$ values and confidence se

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::14bd2bb7053335f91084208c45560b24
https://projecteuclid.org/euclid.bj/1544605238

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On the mean value theorem for estimating functions

Autor: Alexandre G. Patriota

Publikováno v: Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual)
Universidade de São Paulo (USP)
instacron:USP

Feng et al. revealed that the usual mean value theorem (MVT) should not be applied directly to a vector-valued function (e.g., the score function or a general estimating function under a multiparam...

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d5f1ab5d865357baa4e4432e140cf2d5

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On Some Assumptions of the Null Hypothesis Statistical Testing

Autor: Alexandre G. Patriota

Publikováno v: Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual)
Universidade de São Paulo (USP)
instacron:USP

Bayesian and classical statistical approaches are based on different types of logical principles. In order to avoid mistaken inferences and misguided interpretations, the practitioner must respect the inference rules embedded into each statistical me

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::18fadbb5fc77be562855fed73f9631a1
https://doi.org/10.1177/0013164416667979

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Improved estimation in a general multivariate elliptical model

Autor: Tatiane F. N. Melo, Alexandre G. Patriota, Silvia Ferrari

Publikováno v: Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual)
Universidade de São Paulo (USP)
instacron:USP
Braz. J. Probab. Stat. 32, no. 1 (2018), 44-68

The problem of reducing the bias of maximum likelihood estimator in a general multivariate elliptical regression model is considered. The model is very flexible and allows the mean vector and the dispersion matrix to have parameters in common. Many f

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8d71ff5cb27eadc8595f9aa33344f036

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ANOCVA: A Nonparametric Statistical Test to Compare Clustering Structures

Autor: Maciel Calebe Vidal, Alexandre G. Patriota, Davi Augusto Caetano de Jesus, André Fujita

Publikováno v: Theoretical and Applied Aspects of Systems Biology ISBN: 9783319749730

Clustering is an important tool in biological data investigation. For example, in neuroscience, one major hypothesis is that the presence or not of a disorder can be explained by the differences in how brain’s regions of interest cluster. In molecu

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::8faa0ec9f4b3d21482c021b5e398d826
https://doi.org/10.1007/978-3-319-74974-7_6

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Improved hypothesis testing in a general multivariate elliptical model

Autor: Tatiane F. N. Melo, Alexandre G. Patriota, Silvia Ferrari

Publikováno v: Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual)
Universidade de São Paulo (USP)
instacron:USP

This paper investigates improved testing inferences under a general multivariate elliptical regression model. The model is very flexible in terms of the specification of the mean vector and the dispersion matrix, and of the choice of the error distri

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b725473d07af12af92a201efd5c14c3d

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A non-parametric method to estimate the number of clusters

Autor: André Fujita, Alexandre G. Patriota, Daniel Y. Takahashi

Publikováno v: Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual)
Universidade de São Paulo (USP)
instacron:USP

An important and yet unsolved problem in unsupervised data clustering is how to determine the number of clusters. The proposed slope statistic is a non-parametric and data driven approach for estimating the number of clusters in a dataset. This techn

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::01536c7a5c87fe86a804d7d1f3380f90
https://doi.org/10.1016/j.csda.2013.11.012

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