1 citations · 2 across the 9 of their papers we have counts for
12 papers
Decomposing the spectral density function of an ARMA process
Darlin Soto-Vásquez, Francisco Cuevas-Pacheco, Giovanni Motta +1
Quasi-periodic oscillations are characterized by fitting Lorentzian functions to a nonparametric estimate of the power spectral density, a two-step procedure that separates estimat…
-functions for point processes on complex surfaces
Francisco Cuevas-Pacheco, Scott Ward, Ed Cohen +1
The -function is a fundamental summary statistic for assessing clustering or regularity of point processes in two or three dimensional Euclidean space. In practice, however, man…
Regularized estimation for highly multivariate spatial Gaussian random fields
Francisco Cuevas-Pacheco, Gabriel Riffo, Xavier Emery
Estimating covariance parameters for multivariate spatial Gaussian random fields is computationally challenging, as the number of parameters grows rapidly with the number of variab…
Composite likelihood inference for space-time point processes
Abdollah Jalilian, Francisco Cuevas-Pacheco, Ganggang Xu +1
The dynamics of a rain forest is extremely complex involving births, deaths and growth of trees with complex interactions between trees, animals, climate, and environment. We consi…
Pairwise interaction function estimation of Gibbs point processes using basis expansion
Ismaïla Ba, Jean-François Coeurjolly, Francisco Cuevas-Pacheco
The class of Gibbs point processes (GPP) is a large class of spatial point processes able to model both clustered and repulsive point patterns. They are specified by their conditio…
The -family of covariance functions: A Matérn analogue for modeling random fields on spheres
Alfredo Alegría, Francisco Cuevas-Pacheco, Peter Diggle +1
The Mat{é}rn family of isotropic covariance functions has been central to the theoretical development and application of statistical models for geospatial data. For global data def…
A convolution type model for the intensity of spatial point processes applied to eye-movement data
Francisco Cuevas-Pacheco, Jean-François Coeurjolly, Marie-Hélène Descary
Estimating the first-order intensity function in point pattern analysis is an important problem, and it has been approached so far from different perspectives: parametrically, semi…
Karhunen-Loève Expansions for Axially Symmetric Gaussian Processes: Modeling Strategies and Approximations
Alfredo Alegría, Francisco Cuevas-Pacheco
Axially symmetric processes on spheres, for which the second-order dependency structure may substantially vary with shifts in latitude, are a prominent alternative to model the spa…
Regularized estimation for highly multivariate log Gaussian Cox processes
Achmad Choiruddin, Francisco Cuevas-Pacheco, Jean-François Coeurjolly +1
Statistical inference for highly multivariate point pattern data is challenging due to complex models with large numbers of parameters. In this paper, we develop numerically stable…
Second-order variational equations for spatial point processes with a view to pair correlation function estimation
Jean-François Coeurjolly, Francisco Cuevas-Pacheco, Rasmus Waagepetersen
Second-order variational type equations for spatial point processes are established. In case of log linear parametric models for pair correlation functions, it is demonstrated that…
Structured space-sphere point processes and -functions
Jesper Møller, Heidi S. Christensen, Francisco Cuevas-Pacheco +1
This paper concerns space-sphere point processes, that is, point processes on the product space of (the -dimensional Euclidean space) and (the -di…
Log Gaussian Cox processes on the sphere
Jesper Møller, Francisco Cuevas-Pacheco
A log Gaussian Cox process (LGCP) is a doubly stochastic construction consisting of a Poisson point process with a random log-intensity given by a Gaussian random field. Statistica…