Centro de Gestión de la Calidad y del Cambio
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Risk Scoring Models for Trade Credit in Small and Medium Enterprises
Trade credit refers to providing goods and services on a deferred payment basis. Commercial credit management is a matter of great importance for most small and medium enterprises (SMEs), since it represents a significant portion of their assets. Commercial lending involves assuming some credit risk due to exposure to default. Thus, the management of trade credit and payment delays is strongly related to the liquidation and bankruptcy of enterprises. In this paper we study the relationship between trade credit management and the level of risk in SMEs. Despite its relevance for most SMEs, this problem has not been sufficiently analyzed in the existing literature. After a brief review of existing literature, we use a large database of enterprises to analyze data and propose a multivariate decision-tree model which aims at explaining the level of risk as a function of several variables, both of financial and non-financial nature. Decision trees replace the equation in parametric regression models with a set of rules. This feature is an important aid for the decision process of risk experts, as it allows them to reduce time and then the economic cost of their decisions.
Statistical methods to compare mortality for a group with non-divergent populations. Application to Spanish regions
[EN] This paper develops a model to compare geographical differences in the mortality of related regions, taking into account the interdependence between them. Additionally, the model allows us to provide an adequate solution for studying the mortality of a group pertaining to a larger population. It should therefore be possible to improve the mortality analysis for the regions in a country by taking into account the patterns within that country. Using official data from the Spanish National Institute of Statistics (Instituto Nacional de Estadística, INE), we applied a modification of the Lee¿Carter model to Spanish regions. The results of this model were then compared with other similar models such as the logit Brass, Li and Lee and Russolillo¿Giordano¿Haberman. One interesting feature of our model is its simplicity, as the comparison of mortality patterns is accomplished by means of a simple index.
Fault diagnosis and comparing risk for the steel coil manufacturing process using statistical models for binary data
[EN] Advanced statistical models can help industry to design more economical and rational investment plans. Fault detection and diagnosis is an important problem in continuous hot dip galvanizing. Increasingly stringent quality requirements in the automotive industry also require ongoing efforts in process control to make processes more robust. Robust methods for estimating the quality of galvanized steel coils are an important tool for the comprehensive monitoring of the performance of the manufacturing process. This study applies different statistical regression models: generalized linear models, generalized additive models and classification trees to estimate the quality of galvanized steel coils on the basis of short time histories. The data, consisting of 48 galvanized steel coils, was divided into sets of conforming and nonconforming coils. Five variables were selected for monitoring the process: steel strip velocity and four bath temperatures. The present paper reports a comparative evaluation of statistical models for binary data using Receiver Operating Characteristic (ROC) curves. A ROC curve is a graph or a technique for visualizing, organizing and selecting classifiers based on their performance. The purpose of this paper is to examine their use in research to obtain the best model to predict defective steel coil probability. In relation to the work of other authors who only propose goodness of fit statistics, we should highlight one distinctive feature of the methodology presented here, which is the possibility of comparing the different models with ROC graphs which are based on model classification performance. Finally, the results are validated by bootstrap procedures.
Verificación de la evolución de la mortalidad a través de los años
[ES] Típicos enfoques de análisis y graduación de la mortalidad se basan en la no dinamicidad, y por tanto estabilidad de ésta, para periodos largos de tiempo. Por el contrario, este artículo está enfocado sobre un factor relevante en la evolución de la misma, como es el tiempo cronológico. Así pues, el objetivo de este trabajo es analizar la influencia que tiene el tiempo cronológico en las probabilidades anuales de muerte de un individuo de edad x, qx, cuando las probabilidades están tanto graduadas (suavizadas) como sin graduar (sin suavizar). Para ello, se calculan las estimaciones de dichas probabilidades con datos del INE de la Comunidad Valenciana, referidos estos a diferentes periodos de tiempo. Los resultados del estudio empírico parecen confirmar la existencia de diferencias entre las experiencias de mortalidad y comportamientos correspondientes a periodos de tiempo cronológico distintos tanto para hombres como para mujeres. Estas conclusiones ratifican una vez más, la utilidad de las tablas de mortalidad dinámicas en el ámbito de las operaciones actuariales
Desarrollo de una metodología para la formación y evaluación “continua” en pensamiento crítico
[EN] In this study, we shall describe a teaching experience carried out by a group of lecturers in the Statistics Department. These lecturers are involved in the training and assessment of “critical thinking” skills as part of the institutional project centred on generic skills at the Universitat Politècnica de València. The objective of this experience was to promote the understanding, development and application of the critical thinking skills in the students. Different activities linked to the abilities involved in critical thinking are proposed to the students throughout the academic year. The activities require ongoing assessment from the formative point of view. Thus, the students are aware of their own progress. Additionally, students are evaluated in the exams with questions in different formats (PoliformaT exams, tests and peer review). Suitable materials and activities for training and assessment of critical thinking have been developed. Finally the teaching staff and the students’ opinions have been collected through surveys.
Modelos de Machine Learning y estadística multivariante para predecir la posición de los equipos de primera división
[EN] This research aims to find which models of Machine Learning and Multivariate Statistics have a greater predictive capacity when deciding what the team's classification will be at the end of the season. The teams that competed in the first division of the Bundesliga, Premier League, LaLiga, Ligue 1 and Serie A throughout the 2018-2019 season have been studied. The badly classified teams by the best of the models, the Random Forest with balanced data, were analyzed in-depth to determine the game's actions that caused the classification error. The results indicate that, generally, the effectiveness in front of goal and the possession of the ball are the statistics in which badly classified teams differ the most with the average of their real position. In conclusion, this research shows how Machine Learning and Multivariate Statistical techniques can be used successfully to discriminate between Top and Bottom teams competing in the best leagues in the world
A minimally invasive methodology based on morphometric parameters for day 2 embryo quality assessment
[EN] The risk of multiple pregnancy to maternal fetal health can be minimized by reducing the number of embryos transferred. New tools for selecting embryos with the highest implantation potential should be developed. The aim of this study was to evaluate the ability of morphological and morphometric variables to predict implantation by analysing images of embryos. This was a retrospective study of 135 embryo photographs from 112 IVF ICSI cycles carried out between January and March 2011. The embryos were photographed immediately before transfer using Cronus 3 software. Their images were analysed using the public program ImageJ. Significant effects (P < 0.05), and higher discriminant power to predict implantation were observed for the morphometric embryo variables compared with morphological ones. The features for successfully implanted embryos were as follows: four cells on day 2 of development; all blastomeres with circular shape (roundness factor greater than 0.9), an average zona pellucida thickness of 13 µm and an average of 17695.1 µm2 for the embryo area. Embryo size, which is described by its area and the average roundness factor for each cell, provides two objective variables to consider when predicting implantation. This approach should be further investigated for its potential ability to improve embryo scoring.
Exploring essential variables for successful and unsuccessful football teams in the "Big Five" with multivariate supervised techniques
[EN] This research proposes multivariate techniques for discovering the game actions that contribute to the final ranking of football teams. This study uses data from the "Big Five" teams that competed in the Bundesliga First Division, Premier League, LaLiga, Ligue 1, and Serie A in the 2018-2019 season. The principal component analysis is used for outlier detection and for providing an overall preliminary insight. The statistically significant game actions of the top and bottom teams were studied using three supervised multivariate techniques, namely the partial least squares discriminant analysis, random forest and logistic regression. The partial least squares discriminant analysis model best identifies the variables with the most statistically significant contribution to a team's success or failure. The results were compared with those obtained using two-sample univariate tests (such as the Student's t-test or the Mann-Whitney test), demonstrating the advantages of multivariate approaches over univariate approaches. The results indicate that the top teams have both offensive and defensive power, and emphasise the high number of attacking actions; in contrast, the bottom teams have weak defences and few offensive actions.
Influence of popularity on the transfer fees of football players
[EN] Search popularity, as reported by Google Trends, has previously been demonstrated to be useful when studying many time series. However, its use in cross-section studies is not straightforward because search popularity is not provided in absolute terms but as a normalized index that impedes comparisons. This paper proposes a novel methodology for calculating popularity indicators obtained from Google Trends to improve the prediction of football players' transfer fees. The database is formed by 1428 players who competed in LaLiga, Premier League, Bundesliga, Serie A, and Ligue 1 on the 2018-2019 season. Random forest algorithm and multiple linear regression are used to study the popularity indicators' importance and significativity, respectively. Results showed that the proposed popularity indicators provide significant information to predict players’ transfer fees, as models including such popularity indicators had lower prediction error than those without them. This study's developed method could be used not only for analysts specialized in sports data analysis but for researchers of other fields.
On the use of Statistical Process Control in Monitoring Mortality. An Application to European Countries
[EN] The evolution of mortality is a key global concern from both an economic and social point of view. In particular, being able to detect and predict changes in mortality compared to its expected behaviour as accurately as possible is a desirable goal. In this context, the standardized mortality ratio (SMR) is commonly used in order to measure the mortality of a country with regard to its neighbouring countries at a given moment in time. In this work, we address the study of the evolution of the SMR of a country over time, modelled as a time series. The joint use of time series and statistical process control (SPC) techniques to model and monitor the behaviour of the SMR is explored. Both approaches are relevant and complementary. On one hand, time series are an appropriate tool to study and characterize the evolution of SMR over time and to forecast it. On the other hand, SPC permits detection of significant changes in the trend of the variable being monitored. More precisely, we suggest monitoring the residuals of the fitted time series model using control charts. We present and discuss the results of applying our proposal to mortality data for European countries in a 20-year period. These results show the relevance of our approach and delineate our next research steps. Finally, the use of other approaches combining time series and SPC techniques is also outlined.
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