Browsing by Autor "Carlos Valencia"

Now showing 1 - 11 of 11

A Drought Index as a Way to Improve Hedging Effectiveness for Copula Insurance Models
(RELX Group (Netherlands), 2023) Adriana L. Abrego-Pérez; Carlos Valencia
Bi-Objective Optimal Design of Desalination Plants Considering the Uncertainty of Renewable Energy Sources
(RELX Group (Netherlands), 2023) Carlos Ramírez-Ruiz; Carlos Valencia; Sergio Cabrales; Andrés Felipe Ramírez
Copula autoregressive methodology for multi-lag,multi-site simulation of rainfall
(2020) Andrés Felipe Vargas Ramirez; Carlos Valencia
This work presents a methodology for the synthetic generation of rainfall time series based on the copula autoregressive methodology with multiple lags and for multiple sites. In this model, the multivariate time series is decomposed using pairwise copula functions to represent the whole cross-dependence, spatial and temporal structure of the data. We explore the advantages of using this nonlinear method over more traditional approaches that as an intermediate step transform the data to a normal distribution or usually omit the zero mass characteristics of the data. The use of copulas gives flexibility to represent the serial variability of the observed data on the simulation and allows for more control of the desired properties. We use discrete zero mass density distributions to assess the nature of rainfall, alongside a vector generalized linear model for the evaluation of time series distributions and their time dependence in multiple locations. We found that the copula autoregressive methodology models in a satisfactory manner the characteristics of the data, including its zero mass characteristics. These results will help to better understand the fluctuating nature of rainfall and also help to understand the underlying stochastic process.
Dynamic Effect of Climate Change on Flood Damage Cost in the Andean Region of Colombia Using and Ardl-Ecm Model And Climate Change Projections
(RELX Group (Netherlands), 2024) Camilo A. Rodriguez-Espinoza; Carlos Valencia; Carlos Ramírez-Ruiz; Carlos D. Valencia
Effects of Climate Change on Water Shortage in Bogotá, Colombia
(RELX Group (Netherlands), 2025) Carlos D. Valencia; Carlos Valencia; Carlos Ramírez-Ruiz
Effects of Pollution and Climate Change on Chronic and Acute Respiratory Diseases in Bogota, Colombia
(RELX Group (Netherlands), 2025) Carlos D. Valencia; Carlos Valencia; Carlos Ramírez-Ruiz; Sergio Cabrales
Estimating Market Expectations for Portfolio Selection Using Penalized Statistical Models
(District University of Bogotá, 2020) Carlos Valencia; Diego Hernan Segura-Acosta
The portfolio selection problem can be viewed as an optimization problem that maximizes the risk–return relationship. It consists of a number of elements, such as an objective function, decision variables and input parameters, which are used to predict expected returns and the covariance between the said returns. However, the real values of these parameters cannot be directly observed; thus, estimations based on historical data are required. Historical data, however, can often result in modelling errors when the parameters are replaced by their estimations. We propose to address this by using some regularization mechanisms in the optimization. In addition, we explore the use of implicit information to improve the portfolio performance, such as options market prices, which are a rich source of investor expectations. Accordingly, we propose a new estimator for risk and return that combines historical and implicit information in the portfolio selection problem. We implement the new estimators for the mean-VAR and mean-VaR2 problems using an elastic-net model that reduces the risk of all estimations performed. The results suggest that the model has a good out-of-sample performance that is superior to models with pure historical estimations.
Multi-omic Data Integration Using Multi-project and Multi-profile Kernel Joint Non-negative Matrix Factorization to Identify and Analyze Co-modules in Lung Adenocarcinoma
(2022) Diego Salazar; Sara Aceros; Gabriela Aldana; Carlos Valencia
Multi-omic data integration analyzes a vast amount of biological data and contributes to understanding the biological processes underlying organisms. Multiple machine learning techniques have been proposed to solve this task, including extensions of the joint Non-negative Matrix Factorization (jNMF) method, such as the Multi-project and Multi-profile jNMF (M&M-jNMF). This method jointly factorizes input matrices from two projects into low-rank matrices which have clustering properties. However, the M&M-jNMF method does not capture the non-linear patterns of the data. This paper proposes an extension of the M&M-jNMF approach using projections into high-dimensional spaces through kernel functions; therefore, we propose the M&M-KjNMF method. We compared the standard M&M-jNMF and M&M-KjNMF methods using three different omic profiles of the lung adenocarcinoma data. As M&M-jNMF, we used data from experimental and observational data source. We evaluated the performance of both methods by comparing the cophenetic coefficient, AUC, and biological score. We found that M&M-KjNMF outperforms M&M-jNMF. The new proposed method enables the identification of molecule co-modules enriched in pathways tightly related to lung cancer emergence and progression.
Multi-project and Multi-profile joint Non-negative Matrix Factorization for cancer omic datasets
(European Organization for Nuclear Research, 2021) Diego Salazar; Nataša Pržulj; Carlos Valencia
Motivation: The integration of multi-omic data by using machine learning methods has been focused to solve relevant tasks such as predicting sensitivity to a drug, or subtyping patients. Recent integration methods, such as joint Non-negative Matrix Factorization (jNMF), have allowed researchers to exploit the information contained in the data to unravel the biological processes of multi-omic datasets. Results: We present a novel method called Multi-project and Multi-profile joint Non-negative Matrix Factorization (M&M-jNMF) capable of integrating data from different sources, such as experimental and observational multi-omic data. The method can generate co-clusters between observations, predict profiles and relate latent variables. We applied the method to integrate low-grade glioma omic profiles from The Cancer Genome Atlas (TCGA) and Cell Line Encyclopedia (CCLE) projects. The method allowed to find gene clusters that were mainly enriched in cancer-associated terms. We identified groups of patients and cell lines similar to each other by comparing biological processes. We predicted the drug profile for patients, and we identified genetic signatures for resistant and sensitive tumors to a specific drug.
Multi-project and Multi-profile joint Non-negative Matrix Factorization for cancer omic datasets
(European Organization for Nuclear Research, 2021) Diego Salazar; Nataša Pržulj; Carlos Valencia
Motivation: The integration of multi-omic data by using machine learning methods has been focused to solve relevant tasks such as predicting sensitivity to a drug, or subtyping patients. Recent integration methods, such as joint Non-negative Matrix Factorization (jNMF), have allowed researchers to exploit the information contained in the data to unravel the biological processes of multi-omic datasets. Results: We present a novel method called Multi-project and Multi-profile joint Non-negative Matrix Factorization (M&M-jNMF) capable of integrating data from different sources, such as experimental and observational multi-omic data. The method can generate co-clusters between observations, predict profiles and relate latent variables. We applied the method to integrate low-grade glioma omic profiles from The Cancer Genome Atlas (TCGA) and Cell Line Encyclopedia (CCLE) projects. The method allowed to find gene clusters that were mainly enriched in cancer-associated terms. We identified groups of patients and cell lines similar to each other by comparing biological processes. We predicted the drug profile for patients, and we identified genetic signatures for resistant and sensitive tumors to a specific drug.
Multi-project and Multi-profile joint Non-negative Matrix Factorization for cancer omic datasets
(European Organization for Nuclear Research, 2021) Diego Salazar; Nataša Pržulj; Carlos Valencia
Motivation: The integration of multi-omic data by using machine learning methods has been focused to solve relevant tasks such as predicting sensitivity to a drug, or subtyping patients. Recent integration methods, such as joint Non-negative Matrix Factorization (jNMF), have allowed researchers to exploit the information contained in the data to unravel the biological processes of multi-omic datasets. Results: We present a novel method called Multi-project and Multi-profile joint Non-negative Matrix Factorization (M&M-jNMF) capable of integrating data from different sources, such as experimental and observational multi-omic data. The method can generate co-clusters between observations, predict profiles and relate latent variables. We applied the method to integrate low-grade glioma omic profiles from The Cancer Genome Atlas (TCGA) and Cell Line Encyclopedia (CCLE) projects. The method allowed to find gene clusters that were mainly enriched in cancer-associated terms. We identified groups of patients and cell lines similar to each other by comparing biological processes. We predicted the drug profile for patients, and we identified genetic signatures for resistant and sensitive tumors to a specific drug.