Browsing by Author "Castro Cepero, Luis Mauricio"

Now showing 1 - 6 of 6
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    Automated learning of t factor analysis models with complete and incomplete data
    (2017) Wang, Wan-Lun; Castro Cepero, Luis Mauricio; Lin, Tsung-I
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    Extending finite mixtures of t linear mixed-effects models with concomitant covariates
    (2020) Yang, Y. C.; Lin, T. I.; Castro Cepero, Luis Mauricio; Wang, W. L.
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    Identifying outbreaks in sewer networks: An adaptive sampling scheme under network's uncertainty
    (2024) Baboun Larach, José; Beaudry, Isabelle S.; Castro Cepero, Luis Mauricio; Gutiérrez González, Felipe Iván; Jara Vallejos, Alejandro Antonio; Rubio Orellana, Benjamín Eduardo; Verschae, José
    Motivated by the implementation of a SARS-Cov-2 sewer surveillance system in Chile during the COVID-19 pandemic, we propose a set of mathematical and algorithmic tools that aim to identify the location of an outbreak under uncertainty in the network structure. Given an upper bound on the number of samples we can take on any given day, our framework allows us to detect an unknown infected node by adaptively sampling different network nodes on different days. Crucially, despite the uncertainty of the network, the method allows univocal detection of the infected node, albeit at an extra cost in time. This framework relies on a specific and well-chosen strategy that defines new nodes to test sequentially, with a heuristic that balances the granularity of the information obtained from the samples. We extensively tested our model in real and synthetic networks, showing that the uncertainty of the underlying graph only incurs a limited increase in the number of iterations, indicating that the methodology is applicable in practice.
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    Mixtures of factor analyzers with covariates for modeling multiply censored dependent variables
    (2020) Wang, Wan-Lun; Castro Cepero, Luis Mauricio; Hsieh, Wan-Chen; Lin, Tsung-I
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    Quantile regression for nonlinear mixed effects models : a likelihood based perspective
    (2020) Galarza, C. E.; Castro Cepero, Luis Mauricio; Louzada, F.; Lachos, V. H.
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    Spatiotemporal modeling of count data
    (2021) Morales Navarrete, Diego Fabián; Castro Cepero, Luis Mauricio; Pontificia Universidad Católica de Chile. Facultad de Matemáticas
    Modeling spatial and spatio-temporal data is a challenging task in statistics. In many applications, the observed data can be modeled using Gaussian, skew-Gaussian or even restricted random field models. However, in several fields, such as population genetics, epidemiology, aquaculture, among others, the data of interest are often count data, and therefore the mentioned models are not suitable for the analysis of this type of data. Consequently, there is a need for spatial and spatio-temporal models that are able to properly describe data coming from counting processes. Commonly two approaches are used to model this type of data: generalized linear mixed models (GLMMs) with Gaussian random field (GRF) effects, and copula models. Unfortunately, these approaches do not give an explicit characterization of the count random field such us their q-dimensional distribution or correlation function. It is important to stress that GLMMs models induces a discontinuity in the path. Therefore, the correlation function is not continuous at the origin and samples located nearby are more dissimilar than in the continuous case. Moreover, there are cases in which the copula representation for discrete distributions is not unique, so it is unidentifiable. Hence, to deal with the latter mentioned issues, we propose a novel approach to model spatial and spatio-temporal count data in an efficient and accurate manner. Briefly, starting from independent copies of a “parent” GRF, a set of transformations can be applied, and the result is a non-Gaussian random field. This approach is based on the characterization of count random fields that inherit some of the well-known geometric properties from GRFs. For instance, if one chooses an isotropic correlation function defined in the parent GFR, then the count random fields have an isotropic correlation function. Firstly, we define a general class of count random fields. Then, three particular count random fields are studied. The first one is a Poisson random field, the second one is a count random field that considers excess zeros and the last one is a count random field that considers over-dispersion. Additionally, a simulation study will be developed to assess the performance of the proposed models. In that way, we are going to evaluate them through several simulation scenarios, making variations in the parameters. The results show accurate estimations of the parameters for different scenarios. Additionally, we assess the performance of the optimal linear prediction of the proposed models and it is compared with GLMMs and copula models. The results show that the proposed models have a better performance than GLMMs models and a quite similar performance with copula models. Finally, we analyze two real data applications. The first one considers a zero inflated version of the proposed Poisson random field to deal with excess zeros and the second one considers an over-dispersed count random field.