Zobrazeno 1 - 10
of 160
pro vyhledávání: '"Ricardo Cao"'
Autor:
Beatriz Piñeiro-Lamas, Ana López-Cheda, Ricardo Cao, Laura Ramos-Alonso, Gabriel González-Barbeito, Cayetana Barbeito-Caamaño, Alberto Bouzas-Mosquera
Publikováno v:
Scientific Data, Vol 10, Iss 1, Pp 1-9 (2023)
Abstract This dataset is a result of the collaboration between the University of A Coruña and the University Hospital of A Coruña. It contains information about 531 women diagnosed with HER2+ breast cancer, treated with potentially cardiotoxic onco
Externí odkaz:
https://doaj.org/article/9aea46069d9c4fa79f44f241aa924722
Autor:
Inés Barbeito, Daniel Precioso, María José Sierra, Susana Vegas-Azcárate, Sonia Fernández Balbuena, Begoña Vitoriano, David Goméz-Ullate, Ricardo Cao, Susana Monge, the Study Group for Non-Pharmaceutical Interventions in Spain
Publikováno v:
Frontiers in Public Health, Vol 11 (2023)
BackgroundWe estimated the association between the level of restriction in nine different fields of activity and SARS-CoV-2 transmissibility in Spain, from 15 September 2020 to 9 May 2021.MethodsA stringency index (0–1) was created for each Spanish
Externí odkaz:
https://doaj.org/article/ee4d1091fbda44468ee7f11fa2906cf2
Autor:
Paloma Noelia Trigo-Tasende, Manuel Vaamonde, Kelly Conde-Pérez, Ángel López-Oriona, Elisa F. Álvarez, Borja Freire, Mohammed Nasser-Ali, Inés Barbeito, Soraya Rumbo-Feal, Rubén Reif, Bruno K. Rodiño, José Parama, Laura Tomás, Pili Gallego, Germán Bou, Javier Tarrío-Saavedra, Iago I. Corras, David Posada, Ignacio López de Ulibarri, Juan A. Vallejo, Susana Ladra, Ricardo Cao, Margarita Poza
Publikováno v:
Revista de Salud Ambiental, Vol 22, Iss Especial Congreso (2022)
Externí odkaz:
https://doaj.org/article/e06b8de30f35475cb9826488a2b1c327
Autor:
Daniel Barreiro‐Ures, Mario Francisco‐Fernández, Ricardo Cao, Basilio B. Fraguela, Ramón Doallo, José Luis González‐Andújar, Miguel Reyes
Publikováno v:
Ecology and Evolution, Vol 9, Iss 19, Pp 10903-10915 (2019)
Abstract Weed scientists are usually interested in the study of the distribution and density functions of the random variable that relates weed emergence with environmental indices like the hydrothermal time (HTT). However, in many situations, experi
Externí odkaz:
https://doaj.org/article/c846ee26911344138a3695748cd25fc2
Publikováno v:
Mathematics, Vol 10, Iss 9, p 1523 (2022)
For a fixed time, t, and a horizon time, b, the probability of default (PD) measures the probability that an obligor, that has paid his/her credit until time t, runs into arrears not later that time t+b. This probability is one of the most crucial el
Externí odkaz:
https://doaj.org/article/23ad9427fed84126aac099ee34cefd50
Publikováno v:
Proceedings, Vol 54, Iss 1, p 55 (2020)
In this work a doubly smoothed probability of default (PD) estimator is proposed based on a smoothed version of the survival Beran’s estimator. The asymptotic properties of both the smoothed survival and PD estimators are proved and their behaviour
Externí odkaz:
https://doaj.org/article/5270b79938e54981bddda4219cfcdcb0
Publikováno v:
Proceedings, Vol 21, Iss 1, p 42 (2019)
There exist many different methods to choose the bandwidth in kernel regression. If, however, the target is regression based prediction for samples or populations with potentially different distributions, then the existing methods can easily be subop
Externí odkaz:
https://doaj.org/article/0e0add10808048289b346842402ec576
Publikováno v:
Proceedings, Vol 2, Iss 18, p 1166 (2018)
In the context of nonparametric regression estimation, the behaviour of kernel methods such as the Nadaraya-Watson or local linear estimators is heavily influenced by the value of the bandwidth parameter, which determines the trade-off between bias a
Externí odkaz:
https://doaj.org/article/ece7ae4358be49aea0d863f895332c13
Autor:
Inés Barbeito, Ricardo Cao
Publikováno v:
Proceedings, Vol 2, Iss 18, p 1164 (2018)
Bootstrap methods are used for bandwidth selection in: (1) nonparametric kernel density estimation with dependent data (smoothed stationary bootstrap and smoothed moving blocks bootstrap), and (2) nonparametric kernel hazard rate estimation (smoothed
Externí odkaz:
https://doaj.org/article/6fc18c33b27144d08279a260cfd1de2f
Publikováno v:
Proceedings, Vol 2, Iss 18, p 1181 (2018)
A completely nonparametric method for the estimation of mixture cure models is proposed. Nonparametric estimators for the cure probability (incidence) and for the survival function of the uncured population (latency) are introduced. In addition, a bo
Externí odkaz:
https://doaj.org/article/9586f94990e9492e86c978aa4f208679