Browsing by Author "Quinlan, Jose J."

Now showing 1 - 4 of 4
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    Density regression using repulsive distributions
    (2018) Quinlan, Jose J.; Page, Garritt L.; Quintana Quintana, Fernando
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    Joint Random Partition Models for Multivariate Change Point Analysis
    (2024) Quinlan, Jose J.; Page, Garritt L.; Castro, Luis M.
    Change point analyses are concerned with identifying positions of an ordered stochastic process that undergo abrupt local changes of some underly-ing distribution. When multiple processes are observed, it is often the case that information regarding the change point positions is shared across the different processes. This work describes a method that takes advantage of this type of infor-mation. Since the number and position of change points can be described through a partition with contiguous clusters, our approach develops a joint model for these types of partitions. We describe computational strategies associated with our ap-proach and illustrate improved performance in detecting change points through a small simulation study. We then apply our method to a financial data set of emerging markets in Latin America and highlight interesting insights discovered due to the correlation between change point locations among these economies.
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    Modeling wildfires via marked spatio-temporal Poisson processes
    (2021) Quinlan, Jose J.; Diaz-Avalos, Carlos; Mena, Ramses H.
    From a statistical viewpoint, characteristics such as ignition time, location and duration are relevant components for wildfire modeling. The observed ignition sites and starting times constitute a space-time point pattern, and a natural framework to model this type of data is via point processes. In this work, we propose a marked Poisson process to model fire patterns in space-time, considering durations as marks. The collected data correspond to fires observed in the Valencian Community, Spain, between 2010 and 2015. The methodology relies on writing the intensity function of such a process, jointly for starting times, locations and durations, as a weighted Dirichlet process mixture model. A particular choice of the kernel that determines such mixture was made, compatible with data features. We conducted posterior inference on some characteristics of interest for understanding wildfire behavior, showing high flexibility to emulate data patterns.
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    On a class of repulsive mixture models
    (2020) Quinlan, Jose J.; Quintana Quintana, Fernando; Page, Garritt L.