2016: Vol. 40, Núm. 2
http://hdl.handle.net/2117/107525
2023-06-09T01:34:00ZSmoothed landmark estimators of the transition probabilities
http://hdl.handle.net/2117/112750
Smoothed landmark estimators of the transition probabilities
Meira-Machado, Luís
2018-01-12T15:32:03ZMeira-Machado, LuísLog-ratio methods in mixture models for compositional data sets
http://hdl.handle.net/2117/112749
Log-ratio methods in mixture models for compositional data sets
Comas Cufí, Marc; Martín-Fernández, Josep Antoni; Mateu-Figueras, Glòria
When traditional methods are applied to compositional data misleading and incoherent results could be obtained. Finite mixtures of multivariate distributions are becoming increasingly important nowadays. In this paper, traditional strategies to fit a mixture model into compositional data sets are revisited and the major difficulties are detailed. A new proposal using a mixture of distributions defined on orthonormal log-ratio coordinates is introduced. A real data set analysis is presented to illustrate and compare the different methodologies.
2018-01-12T15:30:12ZComas Cufí, MarcMartín-Fernández, Josep AntoniMateu-Figueras, GlòriaWhen traditional methods are applied to compositional data misleading and incoherent results could be obtained. Finite mixtures of multivariate distributions are becoming increasingly important nowadays. In this paper, traditional strategies to fit a mixture model into compositional data sets are revisited and the major difficulties are detailed. A new proposal using a mixture of distributions defined on orthonormal log-ratio coordinates is introduced. A real data set analysis is presented to illustrate and compare the different methodologies.Using robust FPCA to identify outliers in functional time series, with applications to the electricity market
http://hdl.handle.net/2117/112748
Using robust FPCA to identify outliers in functional time series, with applications to the electricity market
Vilar, Juan M.; Raña, Paula; Aneiros, Germán
This study proposes two methods for detecting outliers in functional time series. Both methods take dependence in the data into account and are based on robust functional principal component analysis. One method seeks outliers in the series of projections on the first principal component. The other obtains uncontaminated forecasts for each data set and determines that those observations whose residuals have an unusually high norm are considered outliers. A simulation study shows the performance of these proposed procedures and the need to take dependence in the time series into account. Finally, the usefulness of our methodology is illustrated in two real datasets from the electricity market: daily curves of electricity demand and price in mainland Spain, for the year 2012.
2018-01-12T15:29:04ZVilar, Juan M.Raña, PaulaAneiros, GermánThis study proposes two methods for detecting outliers in functional time series. Both methods take dependence in the data into account and are based on robust functional principal component analysis. One method seeks outliers in the series of projections on the first principal component. The other obtains uncontaminated forecasts for each data set and determines that those observations whose residuals have an unusually high norm are considered outliers. A simulation study shows the performance of these proposed procedures and the need to take dependence in the time series into account. Finally, the usefulness of our methodology is illustrated in two real datasets from the electricity market: daily curves of electricity demand and price in mainland Spain, for the year 2012.Modelling extreme values by the residual coefficient of variation
http://hdl.handle.net/2117/112747
Modelling extreme values by the residual coefficient of variation
del Castillo, Joan; Padilla, Maria
2018-01-12T15:28:03Zdel Castillo, JoanPadilla, MariaA construction of continuous-time ARMA models by iterations of Ornstein-Uhlenbeck processes
http://hdl.handle.net/2117/112746
A construction of continuous-time ARMA models by iterations of Ornstein-Uhlenbeck processes
Arratia Quesada, Argimiro Alejandro; Cabaña, Alejandra; Cabaña, Enrique M.
2018-01-12T15:26:51ZArratia Quesada, Argimiro AlejandroCabaña, AlejandraCabaña, Enrique M.Kernel-based estimation of P(X >Y) in ranked set sampling
http://hdl.handle.net/2117/112745
Kernel-based estimation of P(X >Y) in ranked set sampling
Mahdizadeh, Mahdi; Zamanzade, Ehsan
2018-01-12T15:25:22ZMahdizadeh, MahdiZamanzade, EhsanImproving the resolution of the simple assembly line balancing problem type E
http://hdl.handle.net/2117/112744
Improving the resolution of the simple assembly line balancing problem type E
Corominas, Albert; García-Villoria, Alberto; Pastor Moreno, Rafael
The simple assembly line balancing problem type E (abbreviated as SALBP-E) occurs when the number of workstations and the cycle time are variables and the objective is to maximise the line efficiency. In contrast with other types of SALBPs, SALBP-E has received little attention in the literature. In order to solve optimally SALBP-E, we propose a mixed integer liner programming model and an iterative procedure. Since SALBP-E is NP-hard, we also propose heuristics derived from the aforementioned procedures for solving larger instances. An extensive experimentation is carried out and its results show the improvement of the SALBP-E resolution.
2018-01-12T15:23:33ZCorominas, AlbertGarcía-Villoria, AlbertoPastor Moreno, RafaelThe simple assembly line balancing problem type E (abbreviated as SALBP-E) occurs when the number of workstations and the cycle time are variables and the objective is to maximise the line efficiency. In contrast with other types of SALBPs, SALBP-E has received little attention in the literature. In order to solve optimally SALBP-E, we propose a mixed integer liner programming model and an iterative procedure. Since SALBP-E is NP-hard, we also propose heuristics derived from the aforementioned procedures for solving larger instances. An extensive experimentation is carried out and its results show the improvement of the SALBP-E resolution.