2025-01-09
Besalú, Mireia, Gómez Melis, Guadalupe, Besalú, Mireia, Gómez Melis, Guadalupe
dc.title: Second-order Markov multistate models dc.contributor.author: Besalú, Mireia; Gómez Melis, Guadalupe dc.description.abstract: Multistate models are well developed for continuous and discrete times under a first order Markov assumption. Motivated by a cohort of COVID-19 patients, a multistate model was designed based on 14 transitions among 7 states of a patient. Since a preliminary analysis showed that the frst-order Markov condition was not met for some transitions, we have developed a second-order Markov model where the future evolution not only depends on the state at the current time but also on the state at the preceding time. Under a discrete time analysis, assuming homogeneity and that past information is restricted to two consecutive times, we expanded the transition probability matrix and proposed an extension of the Chapman-Kolmogorov equations. We propose two estimators for the second-order transition probabilities and illustrate them within the cohort of COVID-19 patients.
2025-01-09
Rueda, María del Mar, Cobo, Beatriz, Rueda-Sánchez, Jorge Luis, Ferri-García, Ramon, Castro-Martín, Luis, Rueda, María del Mar, Cobo, Beatriz, Rueda-Sánchez, Jorge Luis, Ferri-García, Ramon, Castro-Martín, Luis
dc.title: Kernel Weighting for blending probability and non-probability survey samples dc.contributor.author: Rueda, María del Mar; Cobo, Beatriz; Rueda-Sánchez, Jorge Luis; Ferri-García, Ramon; Castro-Martín, Luis dc.description.abstract: In this paper we review some methods proposed in the literature for combining a nonprobability and a probability sample with the purpose of obtaining an estimator with a smaller bias and standard error than the estimators that can be obtained using only the probability sample. We propose a new methodology based on the kernel weighting method. We discuss the properties of the new estimator when there is only selection bias and when there are both coverage and selection biases. We perform an extensive simulation study to better understand the behaviour of the proposed estimator.
2025-01-09
Pavlů, Ivana, Menafoglio, Alessandra, Bongiorno, Enea, Hron, Karel, Pavlů, Ivana, Menafoglio, Alessandra, Bongiorno, Enea, Hron, Karel
dc.title: Classification of probability density functions in the framework of Bayes spaces: methods and applications dc.contributor.author: Pavlů, Ivana; Menafoglio, Alessandra; Bongiorno, Enea; Hron, Karel dc.description.abstract: The process of supervised classification when the data set consists of probability density functions is studied. Due to the relative information contained in densities, it is necessary to convert the functional data analysis methods into an appropriate framework, here represented by the Bayes spaces. This work develops Bayes space counterparts to a set of commonly used functional methods with a focus on classification. Hereby, a clear guideline is provided on how some classification approaches can be adapted for the case of densities. Comparison of the methods is based on simulation studies and real-world applications, reflecting their respective strengths and weaknesses.
2025-01-07
Lindgren, Finn, Bakka, Haakon, Bolin, David, Krainski, Elias, Rue, Håvard, Lindgren, Finn, Bakka, Haakon, Bolin, David, Krainski, Elias, Rue, Håvard
dc.title: A diffusion-based spatio-temporal extension of Gaussian Matérn fields dc.contributor.author: Lindgren, Finn; Bakka, Haakon; Bolin, David; Krainski, Elias; Rue, Håvard dc.description.abstract: Gaussian random fields with Matérn covariance functions are popular models in spatial statistics and machine learning. In this work, we develop a spatio-temporal extension of the Gaussian Matérn fields formulated as solutions to a stochastic partial differential equation. The spatially stationary subset of the models have marginal spatial Matérn covariances, and the model also extends to Whittle-Matérn fields on curved manifolds, and to more general non-stationary fields. In addition to the parameters of the spatial dependence (variance, smoothness, and practical correlation range) it additionally has parameters controlling the practical correlation range in time, the smoothness in time, and the type of non-separability of the spatio-temporal covariance. Through the separability parameter, the model also allows for separable covariance functions. We provide a sparse representation based on a finite element approximation, that is well suited for statistical inference and which is implemented in the R-INLA software. The flexibility of the model is illustrated in an application to spatio-temporal modeling of global temperature data.
2025-01-07
Iparragirre, Amaia, Barrio, Irantzu, Aramendi, Jorge, Arostegui, Inmaculada, Iparragirre, Amaia, Barrio, Irantzu, Aramendi, Jorge, Arostegui, Inmaculada
dc.title: Estimation of logistic regression parameters for complex survey data: simulation study based on real survey data dc.contributor.author: Iparragirre, Amaia; Barrio, Irantzu; Aramendi, Jorge; Arostegui, Inmaculada dc.description.abstract: In complex survey data, each sampled observation has assigned a sampling weight, indicating the number of units that it represents in the population. Whether sampling weights should or not be considered in the estimation process of model parameters is a question that still continues to generate much discussion among researchers in different fields. We aim to contribute to this debate by means of a real data based simulation study in the framework of logistic regression models. In order to study their performance, three methods have been considered for estimating the coefficients of the logistic regression model: a) the unweighted model, b) the weighted model, and c) the unweighted mixed model. The results suggest the use of the weighted logistic regression model is superior, showing the importance of using sampling weights in the estimation of the model parameters.