14 documents found, page 1 of 2
Symmetry and complexity in gene association networks using the generalized corr...
Ospina, Raydonal; Xavier, Cleber M.; Esteves, Gustavo H.; Espinheira, Patrícia L.; Castro, Cecilia; Leiva, VíctorHigh-dimensional gene expression data cause challenges for traditional statistical tools, particularly when dealing with non-linear relationships and outliers. The present study addresses these challenges by employing a generalized correlation coefficient (GCC) that incorporates a flexibility parameter, allowing it to adapt to varying levels of symmetry and asymmetry in the data distribution. This adaptability ...
A statistical methodology for evaluating asymmetry after normalization with app...
Leiva, Víctor; Corzo, Jimmy; Vergara, Myrian E.; Ospina, Raydonal; Castro, CeciliaThis study evaluates the symmetry of data distributions after normalization, focusing on various statistical tests, including a few explored test named Rp. We apply normalization techniques, such as variance stabilizing transformations, to ribonucleic acid sequencing data with varying sample sizes to assess their effectiveness in achieving symmetric data distributions. Our findings reveal that while normalizati...
Robust semi-parametric inference for two-stage production models: a beta regres...
Ospina, Raydonal; Baltazar, Samuel G. F.; Leiva, Víctor; Figueroa-Zúñiga, Jorge; Castro, CecíliaThe data envelopment analysis is related to a non-parametric mathematical tool used to assess the relative efficiency of productive units. In different studies on productive efficiency, it is common to employ semi-parametric procedures in two stages to determine whether any exogenous factors of interest affect the performance of productive units. However, some of these procedures, particularly those based on co...
On the use of machine learning techniques and non-invasive indicators for class...
Ospina, Raydonal; Ferreira, Adenice G. O.; Oliveira, Hélio M. de; Leiva, Víctor; Castro, CecíliaThis research aims to enhance the classification and prediction of ischemic heart diseases using machine learning techniques, with a focus on resource efficiency and clinical applicability. Specifically, we introduce novel non-invasive indicators known as Campello de Souza features, which require only a tensiometer and a clock for data collection. These features were evaluated using a comprehensive dataset of h...
Similarity-based predictive models: Sensitivity analysis and a biological appli...
Sanchez, Jeniffer D.; Rêgo, Leandro C.; Ospina, Raydonal; Leiva, Víctor; Chesneau, Christophe; Castro, CecíliaPredictive models based on empirical similarity are instrumental in biology and data science, where the premise is to measure the likeness of one observation with others in the same dataset. Biological datasets often encompass data that can be categorized. When using empirical similarity-based predictive models, two strategies for handling categorical covariates exist. The first strategy retains categorical cov...
Quasi-Cauchy Regression Modeling for fractiles based on data supported in the u...
Oliveira, José Sérgio Casé de; Ospina, Raydonal; Leiva, Víctor; Figueroa-Zúñiga, Jorge; Castro, CecíliaA fractile is a location on a probability density function with the associated surface being a proportion of such a density function. The present study introduces a novel methodological approach to modeling data within the continuous unit interval using fractile or quantile regression. This approach has a unique advantage as it allows for a direct interpretation of the response variable in relation to the expla...
An overview of forecast analysis with ARIMA Models during the COVID-19 Pandemic...
Ospina, Raydonal; Gondim, João A. M.; Leiva, Víctor; Castro, CecíliaThis comprehensive overview focuses on the issues presented by the pandemic due to COVID-19, understanding its spread and the wide-ranging effects of government-imposed restric tions. The overview examines the utility of autoregressive integrated moving average (ARIMA) models, which are often overlooked in pandemic forecasting due to perceived limitations in han dling complex and dynamic scenarios. Our work app...
Biased random-key genetic algorithm with local search applied to the maximum di...
Silva, Geiza; Leite, André; Ospina, Raydonal; Leiva, Víctor; Figueroa-Zúñiga, Jorge; Castro, CecíliaThe maximum diversity problem (MDP) aims to select a subset with a predetermined number of elements from a given set, maximizing the diversity among them. This NP-hard problem requires efficient algorithms that can generate high-quality solutions within reasonable computa tional time. In this study, we propose a novel approach that combines the biased random-key genetic algorithm (BRKGA) with local search to ta...
Fighting the coronavirus: the window of opportunity for non-pharmacological int...
Vasconcelos, Giovani L.; Macêdo, Antônio M. S.; Ospina, Raydonal; Almeida, Francisco A. G.; Duarte-Filho, Gerson C.; Souza, Inês C. L.In this technical note, we present a brief discussion of the main results reported in our paper “Modelling fatality curves of COVID-19 and the effectiveness of intervention strategies”, MedRxiv/2020/051557 (DOI:10.1101/2020.04.02.20051557). In that paper, we applied the Richards growth model (RGM) to describe the fatality curves of the COVID-19 disease for countries that were, up to April 1, 2020, near the end ...
Germany's 'coronavirus anomaly': Statistical evidence that early mass testing l...
Vasconcelos, Giovani L.; Macêdo, Antônio M. S.; Souza, Inês C. L.; Ospina, Raydonal; Almeida, Francisco A. G.; Duarte-Filho , Gerson C.; Holm, ChristianIn this note, we present a statistical analysis of the mortality rates of COVID-19 for several selected European countries. We compare the countries' mortality rates with their respective number of tests as a function of the time since the first death. Our analysis shows that countries that either delayed mass testing, such as Italy, or have not fully adopted it, such as France and the UK, have had much higher ...