Listar GI-MODES - Congresos, conferencias, etc. por título

Mostrando ítems 8-17 de 17

    • Fast Algorithm for Impact Point Selection in Semiparametric Functional Models 

      Novo Díaz, Silvia; Aneiros Pérez, Germán; Vieu, Philippe (M D P I AG, 2019-07-31)
      [Abstract] A new sparse semiparametric functional model is proposed, which tries to incorporate the influence of two functional variables in a scalar response in a quite simple and interpretable way. One of the functional ...

    • Installing Green Artificial Reefs: A Sustainable Challenge 

      Munín-Doce, Alicia; Castro-Santos, Laura; Carral Couce, Luis; Cartelle Barros, Juan José; Camba, C.; Tarrío-Saavedra, Javier (European Association for the Development of Renewable Energies, Environment and Power Quality (EA4EPQ),, 2021-09)
      [Abstract] Green Artificial Reefs (GARs) are marine structures to exploit sea resources in a sustainable way (produce food resources, improve the tourism, etc.). They should be installed on the seabed, process that is ...

    • Nonparametric Inference for Mixture Cure Model When Cure Information Is Partially Available 

      Clarence Safari, Wende; López-de-Ullibarri, Ignacio; Jácome, M. A. (MDPI, 2021)
      [Abstract] We introduce nonparametric estimators to estimate the conditional survival function, cure probability and latency function in the setting of a mixture cure model when the cure status is partially known. For the ...

    • Nonparametric Inference in Mixture Cure Models 

      López-Cheda, Ana; Cao, Ricardo; Jácome, M. A.; Keilegom, Ingrid Van (MDPI, 2018-09)
      [Abstract]: 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 ...

    • Nonparametric Regression Estimation for Circular Data 

      Meilán-Vila, Andrea; Francisco-Fernández, Mario; Crujeiras-Casais, Rosa M.; Panzera, Agnese (M D P I AG, 2019-07-31)
      [Abstract] Non-parametric regression with a circular response variable and a unidimensional linear regressor is a topic which was discussed in the literature. In this work, we extend the results to the case of multivariate ...

    • Robust Methods for Soft Clustering of Multidimensional Time Series 

      López-Oriona, Ángel; D'Urso, Pierpaolo; Vilar, José; Lafuente Rego, Borja Raúl (MDPI, 2021)
      [Abstract] Three robust algorithms for clustering multidimensional time series from the perspective of underlying processes are proposed. The methods are robust extensions of a fuzzy C-means model based on estimates of the ...

    • Sparse Semi-Functional Partial Linear Single-Index Regression 

      Novo Díaz, Silvia; Aneiros Pérez, Germán; Vieu, Philippe (M D P I AG, 2018-09-17)
      [Abstract] The variable selection problem is studied in the sparse semi-functional partial linear model, with single-index type influence of the functional covariate in the response. The penalized least squares procedure ...

    • Testing goodness-of-fit of parametric spatial Trends 

      Meilán-Vila, Andrea; Opsomer, Jean; Francisco-Fernández, Mario; Crujeiras-Casais, Rosa M. (M D P I AG, 2018-09-17)
      [Abstract] The aim of this work is to propose and analyze the behavior of a test statistic to assess a parametric trend surface, that is, a regression model with spatially correlated errors. The asymptotic behavior under ...

    • The Bootstrap for Testing the Equality of Two Multivariate Stochastic Processes with an Application to Financial Markets 

      López-Oriona, Ángel; Vilar, José (MDPI, 2022)
      [Abstract] The problem of testing the equality of generating processes of two multivariate time series is addressed in this work. To this end, we construct two tests based on a distance measure between stochastic processes. ...

    • Unsupervised classification of categorical time series through innovative distances 

      López-Oriona, Ángel; Vilar, José; D'Urso, Pierpaolo (Avestia Publishing, 2022)
      [Abstract]: In this paper, two novel distances for nominal time series are introduced. Both of them are based on features describing the serial dependence patterns between each pair of categories. The first dissimilarity ...