Barros, M., Galea, M., Leiva, V., & Santos-Neto, M. (2018). Generalized Tobit models: diagnostics and application in econometrics. J. Appl. Stat., 45(1), 145–167.
Abstract: The standard Tobit model is constructed under the assumption of a normal distribution and has been widely applied in econometrics. Atypical/extreme data have a harmful effect on the maximum likelihood estimates of the standard Tobit model parameters. Then, we need to count with diagnostic tools to evaluate the effect of extreme data. If they are detected, we must have available a Tobit model that is robust to this type of data. The family of elliptically contoured distributions has the Laplace, logistic, normal and Student-t cases as some of its members. This family has been largely used for providing generalizations of models based on the normal distribution, with excellent practical results. In particular, because the Student-t distribution has an additional parameter, we can adjust the kurtosis of the data, providing robust estimates against extreme data. We propose a methodology based on a generalization of the standard Tobit model with errors following elliptical distributions. Diagnostics in the Tobit model with elliptical errors are developed. We derive residuals and global/local influence methods considering several perturbation schemes. This is important because different diagnostic methods can detect different atypical data. We implement the proposed methodology in an R package. We illustrate the methodology with real-world econometrical data by using the R package, which shows its potential applications. The Tobit model based on the Student-t distribution with a small quantity of degrees of freedom displays an excellent performance reducing the influence of extreme cases in the maximum likelihood estimates in the application presented. It provides new empirical evidence on the capabilities of the Student-t distribution for accommodation of atypical data.
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Ferran, S., Beghelli, A., Huerta-Canepa, G., & Jensen, F. (2018). Correctness assessment of a crowdcoding project in a computer programming introductory course. Comput. Appl. Eng. Educ., 26(1), 162–170.
Abstract: Crowdcoding is a programming model that outsources a software project implementation to the crowd. As educators, we think that crowdcoding could be leveraged as part of the learning path of engineering students from a computer programming introductory course to solve local community problems. The benefits are twofold: on the one hand the students practice the concepts learned in class and, on the other hand, they participate in real-life problems. Nevertheless, several challenges arise when developing a crowdcoding platform, the first one being how to check the correctness of student's code without giving an extra burden to the professors in the course. To overcome this issue, we propose a novel system that does not resort to expert review; neither requires knowing the right answers beforehand. The proposed scheme automatically clusters the student's codes based solely on the output they produce. Our initial results show that the largest cluster contains the same codes selected as correct by the automated and human testing, as long as some conditions apply.
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Garcia-Papani, F., Uribe-Opazo, M. A., Leiva, V., & Aykroyd, R. G. (2017). Birnbaum-Saunders spatial modelling and diagnostics applied to agricultural engineering data. Stoch. Environ. Res. Risk Assess., 31(1), 105–124.
Abstract: Applications of statistical models to describe spatial dependence in geo-referenced data are widespread across many disciplines including the environmental sciences. Most of these applications assume that the data follow a Gaussian distribution. However, in many of them the normality assumption, and even a more general assumption of symmetry, are not appropriate. In non-spatial applications, where the data are uni-modal and positively skewed, the Birnbaum-Saunders (BS) distribution has excelled. This paper proposes a spatial log-linear model based on the BS distribution. Model parameters are estimated using the maximum likelihood method. Local influence diagnostics are derived to assess the sensitivity of the estimators to perturbations in the response variable. As illustration, the proposed model and its diagnostics are used to analyse a real-world agricultural data set, where the spatial variability of phosphorus concentration in the soil is considered-which is extremely important for agricultural management.
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Genco, F., & Genco, G. (2019). Nuclear desalination in Chile: a competitive solution. Desalin. Water Treat., 140, 24–34.
Abstract: Renewable energy sources are considered the main drive for developing at least 70% of the total energy in Chile by 2050. All major international greenhouse gases reduction agreements include growth of renewable energy sources and nuclear power as the only ways to significantly reduce emissions by the decade 2040-50. Chile's energy production matrix still relies heavily on fossil fuels, making very difficult to match the goal targeted by international agreements. For these reasons, the possibility of using nuclear power plants is considered. Small modular reactors (SMRs) in particular seems particularly suitable for a country like Chile for many reasons: SMRs are scalable and can provide energy in remote locations with no or limited grids (Atacama desert); SMRs can cope easily with future demands for expansion, thanks to their modularity; SMRs are cost effective and use all the latest developments in safety. This paper examines, using IAEA DEEP 5 economic software, the costs of nuclear desalinated water produced for the Chilean mining industry. Comparisons with respect to existing fossil fuels solutions show that the final cost is very competitive and allow for significant reduction of CO2 emissions.
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Jordan, A., Hartman, J. D., Bayliss, D., Bakos, G. A., Brahm, R., Bryant, E. M., et al. (2022). HATS-74Ab, HATS-75b, HATS-76b, and HATS-77b: Four Transiting Giant Planets Around K and M Dwarfs. Astron. J., 163(3), 125.
Abstract: The relative rarity of giant planets around low-mass stars compared with solar-type stars is a key prediction from the core-accretion planet formation theory. In this paper we report on the discovery of four gas giant planets that transit low-mass late K and early M dwarfs. The planets HATS-74Ab (TOI737b), HATS-75b (TOI552b), HATS-76b (TOI555b), and HATS-77b (TOI730b) were all discovered from the HATSouth photometric survey and follow-up using TESS and other photometric facilities. We use the new ESPRESSO facility at the VLT to confirm systems and measure their masses. We find that these planets have masses of 1.46 +/- 0.14 MJ, 0.491 +/- 0.039 MJ, 2.629 +/- 0.089 MJ, and 1.374(-0.074)(+0.100) MJ, respectively, and radii of 1.032 +/- 0.021 RJ, 0.884 +/- 0.013 RJ, 1.079 +/- 0.031 RJ, and 1.165 +/- 0.021 RJ, respectively. The planets all orbit close to their host stars with periods ranging from 1.7319 days to 3.0876 days. With further work, we aim to test core-accretion theory by using these and further discoveries to quantify the occurrence rate of giant planets around low-mass host stars.
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Leiva, V., Marchant, C., Ruggeri, F., & Saulo, H. (2015). A criterion for environmental assessment using Birnbaum-Saunders attribute control charts. Environmetrics, 26(7), 463–476.
Abstract: Assessing environmental risk is useful for preventing adverse effects on human health in highly polluted cities. We design a criterion for environmental monitoring based on an attribute control chart for the number of dangerous contaminant levels when the concentration to be monitored follows a Birnbaum-Saunders distribution. This distribution is being widely applied to environmental data. We provide a novel justification for its usage in environmental sciences. The control coefficient and the minimum inspection concentration for the designed criterion are determined to yield the specified in-control average run length, whereas the out-of-control case is obtained according to a shift in the target mean. A simulation study is conducted to evaluate the proposed criterion, which reports its performance to provide earlier alerts of out-of-control processes. An application with real-world environmental data is carried out to validate its coherence with what is reported by the health authority. Copyright (c) 2015 John Wiley & Sons, Ltd.
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Leiva, V., Ruggeri, F., Saulo, H., & Vivanco, J. F. (2017). A methodology based on the Birnbaum-Saunders distribution for reliability analysis applied to nano-materials. Reliab. Eng. Syst. Saf., 157, 192–201.
Abstract: The Birnbaum-Saunders distribution has been widely studied and applied to reliability studies. This paper proposes a novel use of this distribution to analyze the effect on hardness, a material mechanical property, when incorporating nano-particles inside a polymeric bone cement. A plain variety and two modified types of mesoporous silica nano-particles are considered. In biomaterials, one can study the effect of nano-particles on mechanical response reliability. Experimental data collected by the authors from a micro-indentation test about hardness of a commercially available polymeric bone cement are analyzed. Hardness is modeled with the Birnbaum-Saunders distribution and Bayesian inference is performed to derive a methodology, which allows us to evaluate the effect of using nano-particles at different loadings by the R software. (C) 2016 Elsevier Ltd. All rights reserved.
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Leiva, V., Santos-Neto, M., Cysneiros, F. J. A., & Barros, M. (2016). A methodology for stochastic inventory models based on a zero-adjusted Birnbaum-Saunders distribution. Appl. Stoch. Models. Bus. Ind., 32(1), 74–89.
Abstract: The Birnbaum-Saunders (BS) distribution is receiving considerable attention. We propose a methodology for inventory logistics that allows demand data with zeros to be modeled by means of a new discrete-continuous mixture distribution, which is constructed by using a probability mass at zero and a continuous component related to the BS distribution. We obtain some properties of the new mixture distribution and conduct a simulation study to evaluate the performance of the estimators of its parameters. The methodology for stochastic inventory models considers also financial indicators. We illustrate the proposed methodology with two real-world demand data sets. It shows its potential, highlighting the convenience of using it by improving the contribution margins of a Chilean food industry. Copyright (c) 2015 John Wiley & Sons, Ltd.
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Lillo, C., Leiva, V., Nicolis, O., & Aykroyd, R. G. (2018). L-moments of the Birnbaum-Saunders distribution and its extreme value version: estimation, goodness of fit and application to earthquake data. J. Appl. Stat., 45(2), 187–209.
Abstract: Understanding patterns in the frequency of extreme natural events, such as earthquakes, is important as it helps in the prediction of their future occurrence and hence provides better civil protection. Distributions describing these events are known to be heavy tailed and positive skew making standard distributions unsuitable for modelling the frequency of such events. The Birnbaum-Saunders distribution and its extreme value version have been widely studied and applied due to their attractive properties. We derive L-moment equations for these distributions and propose novel methods for parameter estimation, goodness-of-fit assessment and model selection. A simulation study is conducted to evaluate the performance of the L-moment estimators, which is compared to that of the maximum likelihood estimators, demonstrating the superiority of the proposed methods. To illustrate these methods in a practical application, a data analysis of real-world earthquake magnitudes, obtained from the global centroid moment tensor catalogue during 1962-2015, is carried out. This application identifies the extreme value Birnbaum-Saunders distribution as a better model than classic extreme value distributions for describing seismic events.
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Marchant, C., Leiva, V., & Cysneiros, F. J. A. (2016). A Multivariate Log-Linear Model for Birnbaum-Saunders Distributions. IEEE Trans. Reliab., 65(2), 816–827.
Abstract: Univariate Birnbaum-Saunders models have been widely applied to fatigue studies. Calculation of fatigue life is of great importance in determining the reliability of materials. We propose and derive new multivariate generalized Birnbaum-Saunders regression models. We use the maximum likelihood method and the EM algorithm to estimate their parameters. We carry out a simulation study to evaluate the performance of the corresponding maximum likelihood estimators. We illustrate the new models with real-world multivariate fatigue data.
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Marchant, C., Leiva, V., Cysneiros, F. J. A., & Vivanco, J. F. (2016). Diagnostics in multivariate generalized Birnbaum-Saunders regression models. J. Appl. Stat., 43(15), 2829–2849.
Abstract: Birnbaum-Saunders (BS) models are receiving considerable attention in the literature. Multivariate regression models are a useful tool of the multivariate analysis, which takes into account the correlation between variables. Diagnostic analysis is an important aspect to be considered in the statistical modeling. In this paper, we formulate multivariate generalized BS regression models and carry out a diagnostic analysis for these models. We consider the Mahalanobis distance as a global influence measure to detect multivariate outliers and use it for evaluating the adequacy of the distributional assumption. We also consider the local influence approach and study how a perturbation may impact on the estimation of model parameters. We implement the obtained results in the R software, which are illustrated with real-world multivariate data to show their potential applications.
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Rezakhah, M., Moreno, E., & Newman, A. (2020). Practical performance of an open pit mine scheduling model considering blending and stockpiling. Comput. Oper. Res., 115, 12 pp.
Abstract: Open pit mine production scheduling (OPMPS) is a decision problem which seeks to maximize net present value (NPV) by determining the extraction time of each block of ore and/or waste in a deposit and the destination to which this block is sent, e.g., a processing plant or waste dump. Spatial precedence constraints are imposed, as are resource capacities. Stockpiles can be used to maintain low-grade ore for future processing, to store extracted material until processing capacity is available, and/or to blend material based on single or multiple block characteristics (i.e., metal grade and/or contaminant). We adapt an existing integer-linear program to an operational polymetallic (gold and copper) open pit mine, in which the stockpile is used to blend materials based on multiple block characteristics, and call it ((P) over cap (la)). We observe that the linear programming relaxation of our objective function is unimodal for different grade combinations (metals and contaminants) in the stockpile, which allows us to search systematically for an optimal grade combination while exploiting the linear structure of our optimization model. We compare the schedule of ((P) over cap (la)) with that produced by (P-ns) which does not consider stockpiling, and with ((P) over tilde (la)), which controls only the metal content in the stockpile and ignores the contaminant level at the mill and in the stockpile. Our proposed solution technique provides schedules for large instances in a few seconds up to a few minutes with significantly different stockpiling and material flow strategies depending on the model. We show that our model improves the NPV of the project while satisfying operational constraints. (C) 2019 Elsevier Ltd. All rights reserved.
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Santos-Neto, M., Cysneiros, F. J. A., Leiva, V., & Barros, M. (2014). A Reparameterized Birnbaum-Saunders Distribution And Its Moments, Estimation And Applications. REVSTAT-Stat. J., 12(3), 247–272.
Abstract: The Birnbaum-Saunders (BS) distribution is a model that is receiving considerable attention due to its good properties. We provide some results on moments of a reparameterized version of the BS distribution and a generation method of random numbers from this distribution. In addition, we propose estimation and inference for the mentioned parameterization based on maximum likelihood, moment, modified moment and generalized moment methods. By means of a Monte Carlo simulation study, we evaluate the performance of the proposed estimators. We discuss applications of the reparameterized BS distribution from different scientific fields and analyze two real-world data sets to illustrate our results. The simulated and real data are analyzed by using the R software.
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Santos-Neto, M., Cysneiros, F. J. A., Leiva, V., & Barros, M. (2016). Reparameterized Birnbaum-Saunders regression models with varying precision. Electron. J. Stat., 10(2), 2825–2855.
Abstract: We propose a methodology based on a reparameterized Birnbaum-Saunders regression model with varying precision, which generalizes the existing works in the literature on the topic. This methodology includes the estimation of model parameters, hypothesis tests for the precision parameter, a residual analysis and influence diagnostic tools. Simulation studies are conducted to evaluate its performance. We apply it to two real-world case-studies to show its potential with the R software.
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Villalon, J., & Calvo, R. A. (2013). A Decoupled Architecture for Scalability in Text Mining Applications. J. Univers. Comput. Sci., 19(3), 406–427.
Abstract: Sophisticated Text Mining features such as visualization, summarization, and clustering are becoming increasingly common in software applications. In Text Mining, documents are processed using techniques from different areas which can be very expensive in computation cost. This poses a scalability challenge for real-life applications in which users behavior can not be entirely predicted. This paper proposes a decoupled architecture for document processing in Text Mining applications, that allows applications to be scalable for large corpora and real-time processing. It contributes a software architecture designed around these requirements and presents TML, a Text Mining Library that implements the architecture. An experimental evaluation on its scalability using a standard corpus is also presented, and empirical evidence on its performance as part of an automated feedback system for writing tasks used by real students.
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Wanke, P., Ewbank, H., Leiva, V., & Rojas, F. (2016). Inventory management for new products with triangularly distributed demand and lead-time. Comput. Oper. Res., 69, 97–108.
Abstract: This paper proposes a computational methodology to deal with the inventory management of new products by using the triangular distribution for both demand per unit time and lead-time. The distribution for demand during lead-time (or lead-time demand) corresponds to the sum of demands per unit time, which is difficult to obtain. We consider the triangular distribution because it is useful when a distribution is unknown due to data unavailability or problems to collect them. We provide an approach to estimate the probability density function of the unknown lead-time demand distribution and use it to establish the suitable inventory model for new products by optimizing the associated costs. We evaluate the performance of the proposed methodology with simulated and real-world demand data. This methodology may be a decision support tool for managers dealing with the measurement of demand uncertainty in new products. (C) 2015 Elsevier Ltd. All rights reserved.
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