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Copyright of Comunicaciones en Estadística is the property of Universidad Santo Tomas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Cystic echinococcosis (CE) is a parasitic zoonosis caused by Echinococcus granulosus sensu lato. Immunodiagnostic techniques such as Western blot (WB) or enzyme-linked immunosorbent assay (ELISA), with different antigens, can be applied to the diagnosis of CE sheep for epidemiological surveillance purposes in control programs. However, its use is limited by the existence of antigenic cross-reactivity between different species of taeniidae present in sheep. Despite this, the usefulness of establishing surveillance systems based on the identification of infection present in a livestock establishment, known as the Epidemiological Unit (UE), has been postulated. A new ELISA diagnostic technique has been developed and validated using the recombinant EgAgB8/2 antigen for the detection of antibodies against E. granulosus, estimating an OD 0.496 as the cut-off value that optimizes diagnostic sensitivity and specificity. To determine the infection in the EU, a Bayesian model was built, executed with a Monte Carlo Markov chain algorithm, which allowed defining the sample sizes for different expected prevalences to ensure that at least two of the samples are truly positive. In this way, the system can be used to identify the prevalence of infection in the area under control measured as a percentage of EU with the presence of infected sheep (infection present) or individually identify the EU with present transmission, given by presence of infected lambs, on which control measures should be intensified. [ABSTRACT FROM AUTHOR]

Copyright of Mexican Journal of Economics & Finance / Revista Mexicana de Economia y Finanzas is the property of Instituto Mexicano de Ejecutivos de Finanzas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Comunicaciones en Estadística is the property of Universidad Santo Tomas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The approximation of G/G/s models from Markov models M/M/s has been studied in the literature. The study of a queue model is detailed in the present article, using times of arrivals and time service distributed by Weibull whose estimation of parameters was performed with the Bayesian method Monte Carlo Markov chain, specifically the Gibbs sampler. The approximations of this model of waiting lines is evaluated by simulation. This methodology was applied to the case of delivery of refreshments to students of the University of Magdalena in Santa Marta, Colombia. The results show the utility and power to calculate indicators of a queue system when both, the arrival and attention times, are distributed as a Weibull. [ABSTRACT FROM AUTHOR]

The Logistic Regression is a highly used model on different areas of science where the response variable of studied problems is binary. This model can be studied under the Bayesian approach, nonetheless calculations might be complicated even using computation methods, is for that reason we use MCMC methods, which are iterative methods to obtain an approximation of the posterior distribution of model parameters. We use the R software to obtain the distribution with the MCMC method. On this work we implement the logistic regression through a simulation study under the Bayesian Approach applied to the cognitive health state on elders in the state of Guerrero. [ABSTRACT FROM AUTHOR]

Dynamic Bayesian Models are a useful alternative to make forecasts with few historical data, or that permits to complement the little information you have. In this paper we propose an algorithm design to forecast using a dynamic Bayesian model based on Kalman Filter. We illustrate the procedure by applying it to the prediction of daily energy demand of Colombia, which can be useful in systems experiencing failures, or in regions where energy supply is just beginning. The efficiency is determined by the Average Absolute Error Indicator (MAPE), of adjustment and forecast; the last one is less than 3%, appropriate value to show validity of the proposed method. [ABSTRACT FROM AUTHOR]