Journal published since 2008 with the purpose of disseminating original and unpublished papers on any topic of theoretical and/or applied statistics. The journal intends to encourage a culture of statistical research and therefore its target audience are all those researchers using any statistical method in the development of their projects. Topics: Statistics, models, sampling, probability. Periodicity: biannual. ISSN: 2027-3355 Electronic ISSN: 2339-3076 DOI: 10.15332/23393076 e-mail: revistaestadistica@usantotomas.edu.co Indexed in: Redib, Dialnet, Latindex, Circ, Base, Google Scholar, Miar, Ulrich's Periodical Directory, EZB, Seriunam, DRJI, Cite Factor, Academic Resource Index, Current Index to Statistics.
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Editorial: Nuevas Perspectivas Estadísticas para los Desafíos Contemporáneos en Colombia
Editorial del Volumen 18 Número 1. Año 2025
Copyright (c) 2025 Comunicaciones en Estadística https://creativecommons.org/licenses/by-nc-sa/4.0
CAsimulations: Modeling of topological dynamics in a disease using cellular automata
The prediction of the behavior of the disease, the level of affectation in a population and the ways to control it are the most important aspects studied by epidemiology using tools such as historical data and mathematical models. So, our objective is (1) to provide a tool capable of analyzing epidemiological phenomena starting from the most common social interactions within a group of individuals. (2) To provide a methodology to build epidemiological models from patterns and logical rules. (3) Determine the impact of social interactions on the spread of a disease. This paper describes the logical construction of two epidemiological models in cellular automata together with two of their variations based on topological and dynamical principles.
Generalized Dynamic Coefficient Models for Longitudinal Data Analysis in Health: An Application in HIV/AIDS and COVID-19
Longitudinal data analysis is essential when the response variable is measured repeatedly on the same observational unit over time. Traditionally, parametric methods have been employed to estimate coefficients that define the relationship between the linear predictor and the response variable. However, these methods fail when the assumptions regarding the response variable and the model's random components are violated, or when the expected value of the response variable (or its transformation via a link function) cannot be expressed as a known function of the fixed and random effects. In such cases, parametric models may yield conclusions that deviate significantly from the dataset's average trends. Non-parametric regression techniques, which replace fixed parameters with time-dependent smoothed local functions, offer a powerful alternative for longitudinal data analysis. These methods, known as dynamic coefficients or parameters, allow us to establish a more flexible functional relationship between the response variable and the covariates. In this study, we propose estimation and inference techniques for generalized non-parametric dynamic coefficient models, particularly for count response variables. We illustrate our approach through its application in analyzing the effect of viral load on CD4 cell count in HIV/AIDS patients undergoing antiretroviral therapy, as well as in predicting COVID-19 cases.
Spatial Process Models for Social Network Analysis
Statistical modeling of networks enables us to fully characterize the entire system as well as make predictions regarding link formation. Latent models address these tasks by incorporating non-conditional dependencies through random effects. A notable example is the Bayesian spatial process-based model, which is particularly useful for avoiding overfitting issues that may arise in latent distance space models. In this paper, we provide the computational implementation of the model and evaluate its goodness of fit and predictive performance using synthetic networks. The model demonstrates strong capabilities in replicating network statistics and estimating the corresponding latent surface. We also propose an alternative fitting approach using a case-control algorithm. Based on the estimated log-likelihood, the model exhibits good performance in terms of prediction as well as goodness of fit.
Cross-Validation: A Crucial Tool to Enhance the Efficiency of Classification Models in the Medical Field
This study focuses on the implementation of cross-validation techniques to compare various classification models in different scenarios related to biomedical data. These robust classification methods are essential to ensure accurate diagnoses and effective treatments. However, the inherent variability of real-world data in the biomedical field and the complexity of datasets require robust validation approaches. As an investigative proposal, the study addresses the application of cross-validation techniques, including k-fold, Leave-One-Out, StratifiedKFold, and Shuffle Split. These techniques, widely used, aim to enhance the accuracy and generalization of classification models, as well as to identify and mitigate potential biases and overfitting issues. Additionally, various classification algorithms are presented, such as the K-Nearest Neighbors, KNN, Logistic Regression, Random Forest and Decision Tree, to address the challenges posed by the heterogeneous nature of biomedical data collected from four municipalities in Colombia under exposure to heavy metals. Finally, this study analyzes how cross-validation can help increase the robustness of models, enabling a more effective application in clinical environments. This article aims to provide a clear and meaningful overview of cross-validation methods applied to classification algorithms for this type of data, with the goal of adopting models tailored to the specific context.
Bayesian Estimation of the Disappointment Parameter in Discrete Choice Models under the RRM (Random Regret Minimization) Paradigm: Application to Display Design in Large Retail Stores
This study compares two methodological approaches for analyzing discrete choice experiments aimed at identifying the optimal product organization strategy in supermarket shelves, while reducing cognitive complexity for buyers during the purchasing process. The study contrasts the Mixed Multinomial Logit model based on Random Utility Maximization (RUM) with the Bayesian parameter estimation approach of the Mixed Multinomial Logit model under the Random Regret Minimization (RRM) framework. The experiment, structured as a fractional factorial design and conducted with the participation of 748 Colombian shoppers in the toothpaste category, reveals that the Bayesian RRM model provides more sensitive predictions compared to the standard MNL RUM model, particularly for products with medium or small market shares. Additionally, the posterior distributions of parameters affecting variables such as price, benefits, brand, and shelf position enable precise simulations of various scenarios. These simulations capture unobserved heterogeneity in individual consumer preferences, facilitating decision-making in negotiations regarding shelf display strategies with major retailers. Moreover, the posterior distributions offer additional advantages by providing known empirical densities, overcoming the limitations of standard parameter estimation methods in terms of statistical optimization.
Comparison of five machine learning models for the prediction of presidential elections in Colombia: a compositional data perspective
In recent years, numerous studies have employed machine learning techniques and compositional data analysis in various fields of study. However, their integration into electoral analysis remains limited. For this reason, this work combines both approaches by applying five machine learning models: random forest, gradient boosting, support vector machines, k-nearest neighbors, and feedforward neural networks, to predict the results of the presidential elections in Colombia at the municipal level, onsidering the data as compositional. Specifically, it forecasts the vote distribution in each municipality along a unidimensional Left-Right ideological spectrum. This approach aims not only to improve prediction accuracy but also to comtribute a significant advancement in methodologies applied to electoral analysis. The models were trained on 70% of the presidential election data from 2002 to 2022 and evaluated on the remaining 30%. The algorithms demonstrated similar performance across transformations of each ideological spectrum, with variability percentages between 56% and 94% in predicting vote proportions, with the feedforward neural networks model using the centered log-ratio transformation achieving the best results.
Editorial Revista Comunicaciones en Estadística (Volumen 2 Número 17)
Species Classification: An Approach to Improve Accuracy in SnakeIdentification shorttitle
This work aims to apply modern data analysis techniques, particularly neural networks, to improve the classification of snake species in Colombia, using information obtained from records and observations shared on social platforms like Facebook, where users document sightings and characteristics of snakes. In addition to the accurate classification of species, the study is expected to identify patterns in the geographical distribution and behaviors of these species. While neural networks offer a significant advantage in terms of accuracy, they have the disadvantage of being ”black box”models, making them difficult to interpret. Despite this limitation, the study builds on evidence from previous research and data from the online community, with the expectation of improving both species identification and understanding of distribution patterns, contributing to the development of more effective conservation strategies and the protection of biodiversity in Colombia.
Prosecuted crime index for Colombia through geographically andtemporally weighted principal component analysis
Crime analysis is essential to put in place efective security policies. This study focuses on creating an index to assess borough prosecuted crime in Colombia from 2018 to 2023. The index integrates variables of criminal offences along with population density to identify spatiotemporal patterns of criminality and high-risk hotspots through geographically and temporally weighted principal component analysis. The implementation of a robust tool allowed a better explanation of the criminal phenomenon, highlighting the relevance of autocorrelation and spatial-temporal heterogeneity in its analysis. A comparison of spatial multivariate analysis methodologies was carried out by evaluating different kernel functions and bandwidths to optimise the models. The results signal a strong spatial heterogeneity in crime, which suggest the presence of non-random patterns determined by geographical and temporal elements. These findings provide a more precise understanding of the spatial distribution of crime. Finally, a spatiotemporal indicator is created to allow for analysis and comparison across regions and time periods, facilitating the implementation of more effective preventive and corrective measures in the most affected areas.
Use of the Monte Carlo method and inferential statistics in theestimation of geographic areas Cases of Bogot´a and Burlington
This article seeks, through the statistical implementation of the Monte Carlo method, to determine geographic surface areas, taking the cities of Bogot´a and Burlington as case studies using their shapefiles. To fulfill this purpose, the R Studio statistical package is used, which as a programming language and environment has several important advantages when executing parametric methods of statistical inference such as estimation and hypothesis testing, the first is applied through estimation by confidence interval since the result obtained is a random variable with a determined probability of occurrence and respective standard error, the second method is used to verify that the results are valid with respect to the empirical evidence obtained, that is, by comparing the estimated areas with city areas obtained from sources officers. It should be considered that by generating few random numbers the error in the area estimate will be low, but the variance will be greater and the precision of the calculation when carrying out the replications will be low, which can lead to inaccurate results.
Exploring Social Networks in Academic Statistical Production
This work develops a social network analysis of the articles published by the Revista Colombiana de Estad´ıstica, with the aim of uncovering important patterns and characteristics in citation relationships, both among the authors who have been cited or referenced by the journal and among the articles linked to these authors. Through a descriptive analysis and a partitioning of the networks of interest, the fundamental role played not only by internationally recognized authors but also by professors from the National University of Colombia who have dedicated their lives to the field of statistics was discovered.
Are there statistically significant variations in surprise levels based onthe type of magic effect used?
The purpose of this article is to use statistical analysis to gauge the level of preference or aversion that can arise from different magic tricks, including vanishing acts, transformations, swapping objects, or passing through solid objects, when executed using playing cards or currency bills. To address the purpose of this article, we suggest using the PANAS method to measure positive and negative affect. The PANAS test comprises 20 items that describe emotions, with 10 being positive and 10 negative. Respondents rate these items on a scale from 1 to 5 to indicate the extent to which the emotions align with their own feelings. Additionally, the test includes an item to measure viewer curiosity. The analysis of the experiment’s results was conducted using generalized linear models
Case Study of Bayesian Multiple Linear Regression Using the Boston Housing Dataset
Analizamos el conjunto de datos de viviendas de Boston utilizando técnicas de regresión lineal múltiple y mínimos cuadrados ordinarios. Se ajustan varios modelos a los datos para mejorar el poder predictivo de las variables explicativas, evaluando cuidadosamente las suposiciones de cada modelo y realizando un análisis comparativo entre ellos. Identificamos las variables explicativas relevantes que tienen un impacto significativo sobre la variable de respuesta. Además, realizamos experimentos de validación cruzada en modelos seleccionados del análisis. Finalmente, destacamos ciertas limitaciones de los OLS y proponemos el uso de técnicas de regresión ridge como alternativa.
Predicción de la producción diaria de leche en bovinos Gyr a través de métodos de aprendizaje supervisado
The Asociación Colombiana de Criadores de Ganado Cebú - ASOCEBU, has interest in developing a machine to predict total daily milk yield using partial production measurements in Gyr cattle and, in particular, answering two questions: 1) can a reference predictive method be outperformed by locally developed methods? 2) which one of the two partial records (AM or PM) has a better predictive performance? Therefore, the objective of this paper was to develop a predictive machine for daily milk yield in Gyr cattle using partial records, milking interval, days in milk, and parity (n=13806), by implementing supervised learning methods. Besides the reference predictive machine, several combinations of input variables and model or learning method were considered. Artificial neural networks, support vector machines, random forests, and linear regression with location parameters estimated via least squares, or the shrinkage methods Ridge and Lasso were used. The predictive performance (PP) was assessed through crossvalidation using the following error functions: square root of mean square error (RMSE) and mean absolute error (MAE). It was found that an artificial neural network with a single hidden layer and the AM partial record, milking interval, parity and days in milk as input variables had the best PP (RMSE=1.5042, MAE=1.1389), but in general, the performance of the methods was similar. All machines whose parameters were learned using local data outperformed the reference method and the morning partial records showed a better PP than those from the afternoon. These results permit guiding ASOCEBU’s milk control program and generate a “tailormade” method to predict total daily milk yield of Gyr cattle in Colombia, a relevant component of the genetic improvement and productivity modelling programs of this breed.
Copyright (c) 2023 https://creativecommons.org/licenses/by-nc-sa/4.0
Estimaci´on simultánea de la sensibilidad y la especificidad utilizando la metodolog´ía GSK en presencia de covariables
Sensitivity and specificity are measures used to evaluate the performance of diagnostic tests, not only in the health sector with areas such as epidemiology, psychology, and genetics, but also in other fields such as the banking and financial sector, as well as agronomy. Sensitivity indicates the proportion of positive cases that are correctly detected by the test, in other words, sensitivity measures the effectiveness of the test when used in positive individuals, while specificity indicates the proportion of negative cases that are correctly detected by the test, i.e., it measures the effectiveness of the test when used in negative individuals. For the estimation of both quantities, various authors have proposed different methods such as the ’Gold standard’ test, Bayesian approximation, maximum likelihood, or through logistic models. However, these tests only provide marginal type estimates. In this article, we develop a procedure for simultaneously estimating sensitivity and specificity using the GSK methodology in the presence of covariates.
Measurement of the efficiency of the Faculties of the Tunja sectional Santo Tomás University through efficiency models
Efficiency models are techniques used to measure the use of resources and their products or results, commonly called data management unit (DMU), which transform inputs into outputs to measure the efficiency of each study unit. Efficiency models make it possible to compare the different units in terms of results, taking into account their resources. In the present work, INPUTS or input variables are taken for each faculty and the outputs are reviewed, which in this case are the products of the 10 faculties of the Universidad Santo Tomás sectional Tunja, through the application of efficiency frontier models. To meet this objective, information was collected for a specific period through the measurement of different variables framed in the missionary axes of Higher Education in Colombia, namely: Training, Research and Extension or social projection. Two different efficiency frontier model approaches were applied: a parametric approach in which it is assumed that there is a known functional relationship between the input variables (inputs) and the output variables (outputs) by fitting a stochastic frontier model (SFA) and a non-parametric approach in which the data envelope model (DEA) is applied. It was possible to identify which faculties achieve their greatest efficiency with lower inputs, as well as variables that can be improved to achieve greater efficiency in the faculties with lower results. With this work it is expected to provide a management tool that allows generating plans for improvement and optimization of its resources.
Forecasting of COVID-19 in Colombia using recurrent neural networks with long short term memory and gated recurrent units
On march 6 of 2020, the first case of COVID-19 was reported in Colombia. This virus, declared a public health emergency of international importance, has affected different sectors. There is a boom in the number of studies that make forecasts in various aspects that have to do with this virus. The present work shows the theoretical aspects of recurrent neuronal networks and his use to create a 60-day forecast on cumulative cases, cumulative deaths and cumulative recovered, available from march 6 2020 to march 6 2022. Neural networks with GRU and LSTM cells along with the classic RNN were used to make these forecasts.
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Birnbaum-Saunders regression model under the presence of non-parametric effects and non-informative
The main objective of this work is to provide a more flexible version of the Log-Birnbaum-Saunders regression model under the presence of random and non-informative censoring, where it is assumed that the effects of some explanatory variables are nonlinear but their functional form is unknown. The flexibility provided by the proposed model lies in its ability to describe the median life-time distribution by a set of covariates that make use of a sum of arbitrary functions, which is estimated from the data (observations ), thus reducing the possibility of specification error, which allows the estimation of robust parameters under the presence of extremes values or outliers, including non-informative censored observations.
Viewing transactions using functional data analysis
Functional data analysis has begun to gain great importance due to its usefulness in dimensionality reduction, due to its implementation of longitudinal and multivariate methods, where observations can be treated as functions on a closed interval. This paper presents an analysis of the operations carried out by customers of financial institutions through transactional channels, classifying them into face-to-face and virtual, in order to identify characteristics and behavior of customers, both in number and amount. that trade, from a functional approach. A descriptive study of the functional data is carried out, contrasting its results with the variables Gross Domestic Product and Inflation, a cluster is applied through the k-means algorithm that allows identifying groupings between the entities considered within the study, a comparison is made of means for both the number and amount of transactions in the mentioned transactional channels, identifying a point of change in the functional mean. At the end of the analysis, a strong change in the behavior of the data considered is evident, possibly due to the pandemic, which caused transactions through face-to-face channels to decrease, thus strengthening transactions through virtual channels.
Lithological classification from well logs using Machine Learning: Case study Otaraoa Formation, New Zealand
In the oil industry, when exploration well drilling, the uncertainty is excessively high since it is necessary to determine the characteristics of the subsurface and thus the possibilities that oil or gas exists. Indirect methods such as well logs are what provide the basis for geological investigation studies (sedimentary facies, groundwater) which is a complex activity that requires time but allows the evaluator to make decisions. By applying an automatic learning model, we want to reduce this uncertainty and minimize the time in the analysis of well logs. In this study, lithology prediction is investigated using electrical logs (Gamma Rays, Neutron, Density and Photoelectric Effect (PEF)) taken at the Fm. Otaraoa in New Zealand. The training of a Supervised model is carried out where two problems are addressed: the first of identification of two labels (Sand and Clay) and the second of four labels (Sandy Clay, Calcareous Sandy Clay, Clayey Sand and Calcareous Clayey Sand). One well is used to train an algorithm for each case and then two complementary wells are used to test its performance. The results of the Extra Trees Classifier model show that for Problem 1 an Accuracy of 93% was obtained, exceeding the metrics of the model based on rules (Accuracy of 87%), while in Problem 2 the Accuracy was 86%. The model in Problem 1 will learn to recognize the lithology pre-established by the human expert and for Problem 2 it is important to continue feeding the model training with more data from other wells or with core descriptions.
Dynamic linear model to estimate the unemployment rate in Bogot´a D.C.
For this project, the estimation of the unemployment rate in Bogot´a D.C. was developed, and the relationship with some variables of interest was analyzed, all from the frequentist and Bayesian statistical point of view. Initially, a general exploration of time series for the unemployment rate was carried out, where it was evidenced that our variable was not stationary. Therefore, it was decided to apply logarithms and a difference to transform our series and achieve stationarity, necessary for a good estimation. Subsequently, the frequentist estimation was carried out under the Box- Jenkins methodology of various ARIMA models, selecting the best model under the AIC and BIC information criteria. Afterward, the most important assumptions for the residuals of the chosen model were verified. Finally, forecasts were made for six months ahead (July 2022 to December 2022) of the unemployment rate in Bogot´a D.C. On the other hand, the estimation of the unemployment rate from the Bayesian point of view was carried out, starting from the assumption that both the prior and posterior distribution follow a normal distribution. Subsequently, the first-order polynomial estimation and forecasts for six months ahead were made. In conclusion, a comparison was made between the frequentist and Bayesian methodology to conclude which was the best for the estimation of the unemployment rate in Bogot´a D.C. After this, several normal dynamic models were proposed, selecting the best ones and observing their results, thus achieving the best possible model.
Es con gran satisfacción que presentamos a nuestros lectores el primer número del Volumen 17 de la revista Comunicaciones en Estadística, correspondiente al año 2024. En esta edición, hemos reunido una colección de artículos que reflejan la diversidad y profundidad de la investigación estadística en contextos tanto nacionales como internacionales, abarcando una amplia gama de aplicaciones y metodologías que son de gran relevancia en la actualidad. Abrimos este número con un análisis exhaustivo de la dinámica electoral en Estados Unidos, titulado "Trump vs Biden: Análisis de sentimientos de las publicaciones en español realizada en Twitter entres Estados de la Unión en las Elecciones Presidenciales del 2020". Este artículo, de autores internacionales, pone de manifiesto el creciente papel de las tecnologías digitales y las redes sociales en los procesos electorales, ofreciendo una visión detallada de cómo las publicaciones en Twitter pueden reflejar y quizás influir en las percepciones y emociones de los votantes en estados clave. El segundo artículo, "Influencia de las emociones sobre la confianza institucional", nos traslada al contexto colombiano, donde se examina la relación entre las emociones colectivas de los jóvenes y su confianza en las instituciones. Los hallazgos de este estudio son especialmente relevantes en un entorno donde la estabilidad institucional y la movilización social son temas recurrentes, y ofrecen nuevas perspectivas sobre cómo las emociones pueden moldear la confianza en momentos de crisis. La migración rural-urbana en Perú y su impacto en la producción agrícola es el foco del tercer artículo, "Multistage Models for Rural-Urban Migration and Agricultural Production in Peru: 2022". Este trabajo, basado en la Nueva Economía de Migración Laboral, destaca las implicaciones económicas y sociales de la migración, señalando la necesidad de políticas que mitiguen los efectos adversos en las comunidades rurales, particularmente en lo que respecta a la seguridad alimentaria. El uso de modelos bayesianos para el análisis de datos longitudinales y de regresión es explorado en dos artículos de autores nacionales, "Bayesian Hierarchical Gaussian Process Mixtures for Regression Analysis" y "Bayesian Hierarchical Modeling in Linear Regression Settings". Estos trabajos profundizan en la aplicabilidad y flexibilidad de los métodos bayesianos en diferentes contextos, desde estudios clínicos hasta análisis ambientales, demostrando su capacidad para abordar la heterogeneidad y mejorar las predicciones en situaciones complejas. Finalmente, cerramos esta edición con un análisis de la estructura interna del Cuestionario de Motivación y Estrategias de Aprendizaje Forma Corta – MSLQ SF en estudiantes universitarios. Este estudio es fundamental para comprender las dimensiones que influyen en la motivación y las estrategias de aprendizaje, aportando herramientas valiosas para la evaluación y mejora del rendimiento académico en el contexto universitario. Cada uno de estos artículos no solo aporta nuevos conocimientos en sus respectivos campos, sino que también subraya la importancia de la estadística como herramienta fundamental para entender y resolver los desafíos actuales. Esperamos que los hallazgos aquí presentados inspiren nuevas investigaciones y contribuyan al avance de la ciencia estadística. Agradecemos profundamente a los autores por sus contribuciones y a los revisores por su arduo trabajo y dedicación en la evaluación de los manuscritos. Invitamos a nuestros lectores a sumergirse en este número y a seguir participando activamente en la construcción de conocimiento en nuestra disciplina.
Copyright (c) 2024 Comunicaciones en Estadística https://creativecommons.org/licenses/by-nc-sa/4.0
Bayesian Hierarchical Modeling in Linear Regression Settings
Considerando la flexibilidad y aplicabilidad del modelado Bayesiano, se revisan y resumen las principales características de un modelo jerárquico bajo el supuesto usual de intercambiabilidad: presentamos la estructura probabilística del modelo, todos los niveles involucrados en ´el y la distribución condicional completa de cada parámetro del modelo. En este modelo, permitimos que la media de la segunda etapa del modelo tenga una dependencia lineal con un conjunto de covariables mediante un enfoque de regresión. Además, se describe y deriva completamente el algoritmo de muestreo de Gibbs utilizado para obtener muestras de este modelo jerárquico. El estudio de caso es uno en el que caracterizamos en profundidad la superficie media de la temperatura de la mar registrada por 86 dispositivos en el mar Mediterráneo, clasificados por el tipo de dispositivo y la ubicación descrita por la latitud y la longitud. El modelo jerárquico se ajustó considerablemente bien a este conjunto de datos. Los hallazgos derivados de esta aplicación incluyen la descripción de las medias y la variabilidad dentro y entre las temperaturas registradas, evidencia de precisión similar entre dispositivos, diferencias entre tipos de dispositivos y buenas cualidades de predicción del modelo. Finalmente, se prueba la capacidad de predicción del modelo para cada tipo de dispositivo utilizando datos de la Oficina Nacional de Administración Oceánica y Atmosférica.
Motivated strategies for learning Questionnaire – MSLQ SF in university students: analysis of the internal structure
The purpose of this research was to analyze the internal structure of the Motivation and Learning Strategies Questionnaire in university students, taking into account reliability analysis and content validity. For this purpose, 258 students from different universities participated in the study, most of whom were women (60.8 %) and who were selected by a non-probability sampling (purposive sampling). Among the main findings, it can be mentioned that a factorial solution of eight dimensions and 37 items in total was proposed, six dimensions correspond to learning strategies and two to motivation. In addition, Cronbach’s alpha was determined for the total of the scale and an index of .883 (good) was obtained; likewise, it was obtained for each of the dimensions.
confiabilidad y estudiantes universitarios
Bayesian Hierarchical Gaussian Process Mixtures for Regression Analysis
This paper essentially considers Gaussian process (GP) methods for regression analysis in longitudinal studies. The model is indeed a reasonable choice for description and prediction of phenomena involving repeated measurements in which there is evidence of heterogeneity among batches of measurements. First, we provide all the theoretical and practical details behind our modeling strategy. Then, we study the main properties of the model using simulated data. And finally, we apply analyze an AIDS clinical study developed by the AIDS Clinical Trials Group.
Multistage Models for Rural-Urban Migration and Agricultural Production in Peru: 2022
Based on the New Economics of Labor Migration (NELM), rural-urban migration has a significant and adverse impact on the agricultural production of peruvian households. The emigration of a household member to urban areas is related, on average, to a reduction in the value of the household’s agricultural production of up to 48%. It is essential to implement policies that address the economic and social challenges derived from this migration, as well as promote strategies that guarantee the food security of rural communities, recognizing the crucial role of active agricultural workers in this process. Remittances also exert a negative influence on agricultural productive value, although moderate, with a drop of 12% compared to an increase of 10%. This last finding contradicts the results of Taylor (1999) and Bassie et al. (2022), where a positive relationship is established.
Influence of Emotions on Institutional Trust
This document examines the relationship between the collective emotions expressed by young people in Colombia at various times and their trust in the country’s institutions. An aggregated measure of institutional trust is constructed using the Multiple Correspondence Analysis technique. Subsequently, the relationship and impact of emotions on institutional trust are estimated through the fitting of a model adjusted to the Generalized Beta Type 1 density function. The emotions that generate the most social mobilization are anger, sadness, and surprise, with the latter having differential effects depending on age and levels of institutional trust. No differences in perceptions towards institutions were found based on the gender of the respondents. Issues are dynamic and may vary according to predominant emotions. Contrary to expectations, joy does not have a positive effect on institutional trust. Additionally, there is no evidence of emotional cascades, despite dynamic changes in collective emotions during each conjunctural process.
función de densidad Beta Generalizada Tipo 1
Trump vs Biden: Analysis of sentiments of the publications in Spanish made on Twitter in three States of the Union in the 2020 Presidential Elections
Digital technologies have gained great relevance in life in the United States of America, an example is the use of the social network Twitter in the presidential elections that were held in November 2020 in that country. The objective of this work is to analyze the Twitter posts of individuals who published in the states of Florida, California and Texas related to candidates Donald Trump and Joe Biden and describe their behavior towards the candidates in the weeks prior to the 2020 presidential elections. It shows, among other aspects, the variation of the daily sentimental load in the period studied. For the present work, the tweets published between September 7, 2020 and November 2 of the same year were taken. Results were obtained that indicate the predominance of positive sentiments towards President Donald Trump in the states of Florida and Texas, while in the State of California the predominance is towards candidate Joe Biden. An assessment is made of the sentiment of the individuals who published on Twitter related to the vice-presidential formulas of both candidates.
Propuesta de estimación de características sensibles utilizando métodos Bayesianos para la Técnica de Conteo de Ítems de Hussain
En este artículo se presenta una propuesta Bayesiana para mejorar el estimador de Hussain en la Técnica de Conteo de Ítems (TCI), con el objetivo de eliminar o reducir la proporción de estimaciones negativas que presenta este estimador cuando se desea estudiar una característica sensible en particular. Se plantea un estimador analítico empleando como distribuciones previas una distribución beta y la distribución uniforme, el análisis se realiza vía simulación planteando diferentes escenarios para el numero de preguntas en el cuestionario, tamaño de la muestra y la proporción conocida de las preguntas no sensibles, obteniendo finalmente la eliminación total de las estimaciones negativas para la proporción de personas que poseen una característica sensible de interés, presentando reducciones significativas en el coeficiente de variación estimado.