Zobrazeno 1 - 10
of 126
pro vyhledávání: '"Prates, Marcos O"'
The life course perspective in criminology has become prominent last years, offering valuable insights into various patterns of criminal offending and pathways. The study of criminal trajectories aims to understand the beginning, persistence and desi
Externí odkaz:
http://arxiv.org/abs/2405.02666
Imbalanced datasets present a significant challenge for machine learning models, often leading to biased predictions. To address this issue, data augmentation techniques are widely used in natural language processing (NLP) to generate new samples for
Externí odkaz:
http://arxiv.org/abs/2304.10283
Publikováno v:
In Spatial Statistics March 2025 65
Autor:
Schumacher, Fernanda L., Ferreira, Clecio S., Prates, Marcos O., Lachos, Alberto, Lachos, Victor H.
The analysis of complex longitudinal data such as COVID-19 deaths is challenging due to several inherent features: (i) Similarly-shaped profiles with different decay patterns; (ii) Unexplained variation among repeated measurements within each country
Externí odkaz:
http://arxiv.org/abs/2007.00848
Heckman selection model is perhaps the most popular econometric model in the analysis of data with sample selection. The analyses of this model are based on the normality assumption for the error terms, however, in some applications, the distribution
Externí odkaz:
http://arxiv.org/abs/2006.08036
Models that capture the spatial and temporal dynamics are applicable in many science fields. Non-separable spatio-temporal models were introduced in the literature to capture these features. However, these models are generally complicated in construc
Externí odkaz:
http://arxiv.org/abs/2005.05464
The choice of the prior distribution is a key aspect of Bayesian analysis. For the spatial regression setting a subjective prior choice for the parameters may not be trivial, from this perspective, using the objective Bayesian analysis framework a re
Externí odkaz:
http://arxiv.org/abs/2004.04341
Publikováno v:
In Econometrics and Statistics November 2024
This paper presents the development of a spatial block-Nearest Neighbor Gaussian process (block-NNGP) for location-referenced large spatial data. The key idea behind this approach is to divide the spatial domain into several blocks which are dependen
Externí odkaz:
http://arxiv.org/abs/1908.06437
Autor:
Ordoñez, Jose A.1 (AUTHOR), Prates, Marcos O.2 (AUTHOR), Matos, Larissa A.1 (AUTHOR), Lachos, Victor H.3 (AUTHOR) hlachos@uconn.edu
Publikováno v:
Journal of Spatial Science. Jan2024, Vol. 69 Issue 1, p61-79. 19p.