Zobrazeno 1 - 10
of 97
pro vyhledávání: '"Vasconcelos, Gabriel"'
We use machine learning techniques to investigate whether it is possible to replicate the behavior of bank managers who assess the risk of commercial loans made by a large commercial US bank. Even though a typical bank already relies on an algorithmi
Externí odkaz:
http://arxiv.org/abs/2202.04218
Data on hundreds of variables related to individual consumer finance behavior (such as credit card and loan activity) is routinely collected in many countries and plays an important role in lending decisions. We postulate that the detailed nature of
Externí odkaz:
http://arxiv.org/abs/2111.03662
Autor:
Medeiros, Marcelo, Street, Alexandre, Valladão, Davi, Vasconcelos, Gabriel, Zilberman, Eduardo
The number of Covid-19 cases is increasing dramatically worldwide. Therefore, the availability of reliable forecasts for the number of cases in the coming days is of fundamental importance. We propose a simple statistical method for short-term real-t
Externí odkaz:
http://arxiv.org/abs/2004.07977
We provide a new theory for nodewise regression when the residuals from a fitted factor model are used. We apply our results to the analysis of the consistency of Sharpe ratio estimators when there are many assets in a portfolio. We allow for an incr
Externí odkaz:
http://arxiv.org/abs/2002.01800
Publikováno v:
In Journal of Econometrics August 2023 235(2):393-417
Publikováno v:
Repositório Institucional da UFJFUniversidade Federal de Juiz de ForaUFJF.
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Approved for entry into archive by Adriana Oliv
Approved for entry into archive by Adriana Oliv
Externí odkaz:
https://repositorio.ufjf.br/jspui/handle/ufjf/2402
In this paper, we introduce a new machine learning (ML) model for nonlinear regression called the Boosted Smooth Transition Regression Trees (BooST), which is a combination of boosting algorithms with smooth transition regression trees. The main adva
Externí odkaz:
http://arxiv.org/abs/1808.03698
Autor:
Medeiros, Marcelo C., Street, Alexandre, Valladão, Davi, Vasconcelos, Gabriel, Zilberman, Eduardo
Publikováno v:
In International Journal of Forecasting April-June 2022 38(2):467-488