Exploració per autor "Puig, Pedro"
Ara es mostren els items 1-5 de 5
-
A characterization of the innovations of first order autoregressive models
Moriña, David; Puig, Pedro; Valero Baya, Jordi (2015-02-01)
Article
Accés restringit per política de l'editorialSuppose that follows a simple AR(1) model, that is, it can be expressed as , where is a white noise with mean equal to and variance . There are many examples in practice where these assumptions hold very well. Consider . ... -
Cumulated burden of Covid-19 in Spain from a Bayesian perspective
Moriña, David; Fernandez Fontelo, Amanda; Cabaña Nigro, Ana Alejandra; Arratia Quesada, Argimiro Alejandro; Ávalos Villaseñor, Gustavo Eduardo; Puig, Pedro (2021-12)
Article
Accés obertBackground The main goal of this work is to estimate the actual number of cases of Covid-19 in Spain in the period 01-31-2020/06-01-2020 by Autonomous Communities. Based on these estimates, this work allows us to accurately ... -
Estimating the real burden of disease under a pandemic situation: the SARS-CoV2 case
Fernandez Fontelo, Amanda; Moriña, David; Cabaña Nigro, Ana Alejandra; Arratia Quesada, Argimiro Alejandro; Puig, Pedro (Public Library of Science (PLOS), 2020-12-03)
Article
Accés obertThe present paper introduces a new model used to study and analyse the severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) epidemic-reported-data from Spain. This is a Hidden Markov Model whose hidden layer is a ... -
Goodness of fit tests for the skew-Laplace distribution
Puig, Pedro; Stephens, Michael A. (Institut d'Estadística de Catalunya, 2007)
Article
Accés obertThe skew-Laplace distribution is frequently used to fit the logarithm of particle sizes and it is also used in Economics, Engineering, Finance and Biology. We show the Anderson-Darling and Cram´ er-von Mises goodness of ... -
What does a zero mean? understanding false, random and structural zeros in ecology
Blasco Moreno, Anabel; Pérez Casany, Marta; Puig, Pedro; Morante, Maria; Castells, Eva (2019-03-27)
Article
Accés obert1. Zeros (i.e. events that do not happen) are the source of two common phenomena in count data: overdispersion and zero-inflation. Zeros have multiple origins in a dataset: false zeros occur due to errors in the experimental ...