https://hdl.handle.net/10481/15032 Mon, 20 Apr 2026 06:49:46 GMT 2026-04-20T06:49:46Z https://hdl.handle.net/10481/91167 Growth Curves Modelling and Its Application García Burgos, Ana; González-Alzaga, Beatriz; Giménez Asensio, María José; Lacasaña, Marina; Rico Castro, Nuria; Romero Molina, Desirée In this article, we compare two ways of modelling measures of fetal growth. The goal is to impute the missing information for certain ultrasound measurements that are observed at different times and with different numbers of observations. To analyze the effect that other variables have, such as environmental exposure to certain substances or diet, on fetal growth based on these data, we need to handle the information measured at the same instant of time for all the individuals under study, preferably in three time windows of pregnancy (first trimester, week 12; second trimester, week 20; third trimester, week 34). For this, data at these chosen times, in case they are not available, must be imputed from the available information using an appropriate statistical model. One option is to use a linear model, specifically a generalized least squares model that is fitted to the features shown in the data. The other option is to use diffusion processes, estimating their parameters based on the available information. In both options, missing data can be estimated with the unconditional fitted model, conditional on the previous available measurement, or conditional to the closest measurement.; En este artículo, comparamos dos formas de modelar medidas de crecimiento fetal. El objetivo es imputar la información faltante para determinadas mediciones ecográficas que se observan en diferentes momentos y con diferente número de observaciones. Para analizar el efecto que otras variables, como la exposición ambiental a determinadas sustancias o la dieta, tienen sobre el crecimiento fetal a partir de estos datos, debemos manejar la información medida en el mismo instante de tiempo para todos los individuos estudiados, preferiblemente en tres ventanas de tiempo del embarazo (primer trimestre, semana 12; segundo trimestre, semana 20; tercer trimestre, semana 34). Para ello, los datos en esos momentos elegidos, en caso de que no estén disponibles, deberán imputarse a partir de la información disponible utilizando un modelo estadístico adecuado. Una opción es utilizar un modelo lineal, específicamente un modelo de mínimos cuadrados generalizados que se ajuste a las características que se muestran en los datos. La otra opción es utilizar procesos de difusión, estimando sus parámetros en función de la información disponible. En ambas opciones, los datos faltantes se pueden estimar con el modelo ajustado incondicional, condicional a la medición anterior disponible o condicional a la medición más cercana. FQM147-Análisis estadístico de datos multivariantes y procesos estocásticos. https://hdl.handle.net/10481/91167 https://hdl.handle.net/10481/78231 Differences by country in academic production indexed in Scopus on intellectual property and innovation systems (2001-2021) Lis Gutiérrez, Jenny Paola; Marmolejo Martín, Juan Antonio; Barbosa Lugo, Katty Lorena; Pulido-Flórez, Jhonatan Steven This paper aims to establish what are the differences by country in scientific production on intellectual property and innovation systems between 2001 and 2021? We use text mining, non-parametric statistics, and two specialized software (Bibliometrix and VosViewer) to indicate the differences in scientific production by country on innovation systems and intellectual property. We found that scientific production in the Asia Pacific and North American countries is, on average, higher than in Eastern Europe, the Middle East and North Africa, and Sub-Saharan Africa. These last three regions do not exhibit statistically significant differences among themselves. On the other hand, the countries of Western Europe exceed the production levels of the countries of Eastern Europe and Sub-Saharan Africa. We identified that the topics in the scientific production of the most productive countries were related to case studies, technology transfer, triple helix, regional innovation systems, governance, open innovation, competitiveness, and innovation policies. https://hdl.handle.net/10481/78231 https://hdl.handle.net/10481/64264 Signal Estimation with Random Parameter Matrices and Time-correlated Measurement Noises Caballero Águila, R.; Hermoso Carazo, Aurora; Linares Pérez, Josefa This paper is concerned with the least-squares linear estimation problem for a class of discrete-time networked systems whose measurements are perturbed by random parameter matrices and time-correlated additive noise, without requiring a full knowledge of the state-space model generating the signal process, but only information about its mean and covariance functions. Assuming that the measurement additive noise is the output of a known linear systemdriven by white noise, the time-differencing method is used to remove this time-correlated noise and recursive algorithms for the linear filtering and fixed-point smoothing estimators are obtained by an innovation approach. These estimators are optimal in the least-squares sense and, consequently, their accuracy is evaluated by the estimation error covariance matrices, for which recursive formulas are also deduced. The proposed algorithms are easily implementable, as it is shown in the computer simulation example, where they are applied to estimate a signal from measured outputs which, besides including time-correlated additive noise, are affected by the missing measurement phenomenon and multiplicative noise (random uncertainties that can be covered by the current model with random parameter matrices). The computer simulations also illustrate the behaviour of the filtering estimators for different values of the missing measurement probability. https://hdl.handle.net/10481/64264 https://hdl.handle.net/10481/64263 Optimal Filtering Algorithm based on Covariance Information using a Sequential Fusion Approach Caballero Águila, R.; Hermoso Carazo, Aurora; Linares Pérez, Josefa The least-squares linear filtering problem is addressed for discrete-time stochastic signals, whose evolution model is unknown and only the mean and covariance functions of the processes involved in the sensor measurement equations are available instead. The sensor measured outputs are perturbed by additive noise and different uncertainties, which are modelled in a unified way by random parameter matrices. Assuming that, at each sampling time, the noises from the different sensors are cross-correlated with each other, the sequential fusion architecture is adopted and the innovation technique is used to derive an easily implementable recursive filtering algorithm. A simulation example is included to verify the effectiveness of the proposed sequential fusion filter and analyze the influence of the sensor disturbances on the filter performance. https://hdl.handle.net/10481/64263