Departamento de Estadística e Investigación Operativa

URI permanente para esta comunidadhttps://hdl.handle.net/10953/30

En esta Comunidad se recogen los documentos generados por el Departamento de Estadística e Investigación Operativa y que cumplen los requisitos de Copyright para su difusión en acceso abierto.

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A modelling of the number of almazaras by municipality in Andalusia
(2022) Cueva-López, Valentina; Rodríguez-Avi, José; Olmo-Jiménez, María José; Rodríguez-Reinoso, Julia
An almazara (oil mill) is an essential piece in the production of olive oil since it is the place where the olive is milled and the olive oil is obtained. They are usually linked to producer cooperatives. They are structures that require specialized machinery and that on multiple occasions are underutilised, given the presence of several of them at very close distances. In addition, they characterise the mainly olive grove municipalities and their study provides a valuable information of economic interest. From a statistical point of view, the “number of oil mills per municipality” is a count data variable that exhibits overdispersion. In this study, we focus on the oil mills found in municipalities of Andalusia. First, we make a descriptive study of the variable. Second, we model this data according to the most suitable probabilistic model. Finally, several generalized linear regression models based on different geographic and socioeconomic variables are proposed and the best one (using the Akaike information criterion) is selected.
A new approach to truncated regression for count data
(Springer, 2018-12-10) Martínez-Rodríguez, Ana M.; Conde-Sánchez, Antonio; Olmo-Jiménez, María J.
Standard Poisson and negative binomial truncated regression models for count data include the regressors in the mean of the non-truncated distribution. In this paper, a new approach is proposed so that the explanatory variables determine directly the truncated mean. The main advantage is that the regression coefficients in the new models have a straightforward interpretation as the effect of a change in a covariate on the mean of the response variable. A simulation study has been carried out in order to analyze the performance of the proposed truncated regression models versus the standard ones showing that coefficient estimates are now more accurate in the sense that the standard errors are always lower. Also, the simulation study indicates that the estimates obtained with the standard models are biased. An application to real data illustrates the utility of the introduced truncated models in a hurdle model. Although in the example there are slight differences in the results between the two approaches, the proposed one provides a clear interpretation of the coefficient estimates.
An Optimal Linear Fusion Estimation Algorithm of Reduced Dimension for T-Proper Systems with Multiple Packet Dropouts
(MDPI, 2023-04-17) Fernández Alcalá, Rosa María; Jiménez López, José Domingo; Le Bihan, Nicolas; Cheong Took, Clive
This paper analyses the centralized fusion linear estimation problem in multi-sensor systems with multiple packet dropouts and correlated noises. Packet dropouts are modeled by independent Bernoulli distributed random variables. This problem is addressed in the tessarine domain under conditions of 𝕋1 and 𝕋2-properness, which entails a reduction in the dimension of the problem and, consequently, computational savings. The methodology proposed enables us to provide an optimal (in the least-mean-squares sense) linear fusion filtering algorithm for estimating the tessarine state with a lower computational cost than the conventional one devised in the real field. Simulation results illustrate the performance and advantages of the solution proposed in different settings.
Applying the personal and social responsibility model-based program: Differences according to gender between basic psychological needs, motivation, life satisfaction and intention to be physically active
(MDPI, 2019-07-01) Manzano-Sánchez, David; Valero-Valenzuela, Alfonso; Conde-Sánchez, Antonio; Chen, Ming-Yao
The objective of the study was to evaluate the impact of a program based on the Teaching Personal and Social Responsibility (TPSR) on the variables of responsibility, basic psychology needs, motivation, satisfaction with life and the intention to be physically active, as well as the differences of gender. The participants were 85 students (experimental group n = 35, 17 girls and control group n = 50, 28 girls). The students of the experimental group received the TPSR for 8 months within the physical education subject. The findings indicated an improvement in the experimental group in terms of personal responsibility and in the case of female students, in basic psychological needs and intrinsic motivation. In conclusion, the TPSR program can be integrated into the physical education curriculum in order to improve the personal responsibility of students and fulfill their motivation and satisfaction of basic psychological needs.
Biased Regression Algorithms in the Quaternion Domain
(Elsevier, 2024) Ruiz Molina, Juan Carlos; Navarro Moreno, Jesús; Fernández Alcalá, Rosa Mª; Jiménez López, José D.
The ill-conditioned matrix problem in quaternion linear regression models is addressed in this paper and several dimension-reduction based regression methods for circumventing this problem are suggested. The algorithms are formulated in a general way and can be easily adapted to different scenarios: widely linear, semi-widely linear and strictly linear processing, in accordance with the properness properties presented by quaternion random vectors. A comparison with existing solutions is carried out by using both laboratory data and a color face database.
DGLMExtPois: Advances in Dealing with Over and Under-dispersion in a Double GLM Framework
(The R Foundation, 2022-12) Sáez-Castillo, Antonio J.; Conde-Sánchez, Antonio; Martínez, Francisco
In recent years the use of regression models for under-dispersed count data, such as COM-Poisson or hyper-Poisson models, has increased. In this paper the DGLMExtPois package is presented. DGLMExtPois includes a new procedure to estimate the coefficients of a hyper-Poisson regression model within a GLM framework. The estimation process uses a gradient-based algorithm to solve a nonlinear constrained optimization problem. The package also provides an implementation of the COM-Poisson model, proposed by Huang (2017), to make it easy to compare both models. The functionality of the package is illustrated by fitting a model to a real dataset. Furthermore, an experimental comparison is made with other related packages, although none of these packages allow you to fit a hyper-Poisson model.
Distributed fusion filtering for multi-sensor nonlinear networked systems with multiple fading measurements via stochastic communication protocol
(Elsevier, 2024-06-24) Hu, Jun; Hu, Zhibin; Caballero-Águila, Raquel; Yi, Xiaojian
This paper studies the distributed fusion filtering (DFF) issue for a class of nonlinear delayed multi-sensor networked systems (MSNSs) subject to multiple fading measurements (MFMs) under stochastic communication protocol (SCP). The phenomenon of MFMs occurs randomly in the network communication channels and is characterized by a diagonal matrix with certain statistical information. In order to decrease the overload of communication network and save network resources, the SCP that can regulate the information transmission between sensors and estimators is adopted. The primary aim of the tackled problem is to develop the DFF method for nonlinear delayed MSNSs in the presence of MFMs and SCP based on the inverse covariance intersection fusion rule. In addition, the local upper bound (UB) of the filtering error covariance (FEC) is derived and minimized by means of suitably designing the local filter gain. Moreover, the boundedness analysis regarding the local UB is proposed with corresponding theoretical proof. Finally, two simulation examples with comparative illustrations are given to display the usefulness and feasibility of the derived theoretical results.
Distributed fusion filtering for multi-sensor nonlinear networked systems with multiple fading measurements via stochastic communication protocol
(Elsevier, 2024) Hu, Jun; Hu, Zhibin; Caballero-Águila, Raquel; Yi, Xiaojian
This paper studies the distributed fusion filtering (DFF) issue for a class of nonlinear delayed multi-sensor networked systems (MSNSs) subject to multiple fading measurements (MFMs) under stochastic communication protocol (SCP). The phenomenon of MFMs occurs randomly in the network communication channels and is characterized by a diagonal matrix with certain statistical information. In order to decrease the overload of communication network and save network resources, the SCP that can regulate the information transmission between sensors and estimators is adopted. The primary aim of the tackled problem is to develop the DFF method for nonlinear delayed MSNSs in the presence of MFMs and SCP based on the inverse covariance intersection fusion rule. In addition, the local upper bound (UB) of the filtering error covariance (FEC) is derived and minimized by means of suitably designing the local filter gain. Moreover, the boundedness analysis regarding the local UB is proposed with corresponding theoretical proof. Finally, two simulation examples with comparative illustrations are given to display the usefulness and feasibility of the derived theoretical results.
Distributed fusion filtering for uncertain systems with coupled noises, random delays and packet loss prediction compensation
(Taylor & Francis, 2023) Caballero-Águila, Raquel; Linares-Pérez, Josefa
The design of recursive estimation algorithms in networked systems is an important research challenge from both theoretical and practical perspectives. The growing number of application fields are demanding the development of new mathematical models and algorithms that accommodate the effect of the unavoidable network-induced uncertainties. Special relevance have transmission delays and packet dropouts, which may yield a significant degradation in the performance of conventional estimators. This paper discusses the distributed fusion estimation problem in a class of linear stochastic uncertain systems whose measurement noises are cross-correlated and coupled with the process noise. The uncertainty of the system is not only described by additive noises, but also by multiplicative noise in the state equation and random parameter matrices in the measurement model. Both one-step delays and packet dropouts can randomly occur during the transmission of the sensor measurements to the local processors and a compensation strategy based on measurement prediction is used. Under the least-squares criterion and using an innovation approach, a recursive algorithm for the local filtering estimators is designed. These local estimators are then fused at a processing centre, where the distributed fusion filter is generated as the least-squares matrix-weighted linear combination of the local ones.
Distributed optimal fusion filtering for singular systems with random transmission delays and packet dropout compensations
(Elsevier, 2023) Hu, Jun; Wang, Chen; Caballero-Águila, Raquel; Liu, Hongjian
This paper is concerned with the fusion filtering problem for time-varying singular systems with random transmission delays (RTDs) and packet dropout (PD) compensations. Here, the phenomena of RTDs and PDs are both characterized by Bernoulli distributed random variables with different probabilities. Generally, the current sensor measurement and one-step delayed sensor measurement can be received by filter. When the sensor measurement is lost, based on the strategy of PD compensations, the one-step predictor of current sensor measurement is used as compensator. Then, the new augmented systems with stochastic parameter matrices and correlated noises are introduced based on the measurement compensation model. Utilizing the innovation analysis approach, the local filters (LFs) dependent on probabilities and corresponding estimation error covariance matrices are derived for augmented systems. Moreover, the matrix-weighted distributed fusion filter (DFF) is designed for original singular systems on the basis of the state transformation. Compared with the LFs, it is not difficult to see that the presented DFF has better precision. In the end, some comparison simulation experiments are carried out to verify the effectiveness of the proposed fusion filtering algorithm.
Distributed resilient fusion filtering for nonlinear systems with multiple missing measurements via dynamic event-triggered mechanism
(Elsevier, 2023) Hu, Jun; Hu, Zhibin; Caballero-Águila, Raquel; Chen, Cai; Fan, Shuting; Yi, Xiaojian
This paper investigates the distributed resilient fusion filtering (DRFF) issue under inverse covariance intersection (ICI) fusion criterion and dynamic event-triggered mechanisms (DETMs), where the physical plant is described by stochastic nonlinear multi-sensor networked systems (MSNSs) with time-varying system parameters and multiple missing measurements (MMMs). The measurements from various sensor nodes to the fusion center may undergo the missing data, where this phenomenon is depicted by means of random variables governed by certain statistical principles. In addition, the DETM is adopted to regulate the communication process from each sensor node to fusion center, which can alleviate the network transmission situations with communication overload and energy consumption limitation. The purpose of the addressed issue is to construct a set of local resilient filters (LRFs) for stochastic nonlinear MSNSs with MMMs via the DETM, which can guarantee that the minimized upper bounds are derived and the desirable filter gain with easy-to-implementation form is given. Subsequently, via the obtained LRFs, a unified framework of the DRFF approach is formulated through using the ICI fusion criterion. In addition, the monotonicity analysis of the obtained upper bound in regard to the triggered parameter is examined by providing rigorous theoretical proof. Finally, the simulations with comparison experiment are provided to illustrate the validity of presented DRFF technique.
Filtering and smoothing estimation algorithms from uncertain nonlinear observations with time-correlated additive noise and random deception attacks
(Taylor & Francis, 2024-03-19) Caballero-Águila, R.; Hu, J.; Linares-Pérez, J.
This paper discusses the problem of estimating a stochastic signal from nonlinear uncertain observations with time-correlated additive noise described by a first-order Markov process. Random deception attacks are assumed to be launched by an adversary, and both this phenomenon and the uncertainty in the observations are modelled by two sets of Bernoulli random variables. Under the assumption that the evolution model generating the signal to be estimated is unknown and only the mean and covariance functions of the processes involved in the observation equation are available, recursive algorithms based on linear approximations of the real observations are proposed for the least-squares filtering and fixed-point smoothing problems. Finally, the feasibility and effectiveness of the developed estimation algorithms are verified by a numerical simulation example, where the impact of uncertain observation and deception attack probabilities on estimation accuracy is evaluated.
How do sentiments affect virality on Twitter?
(The Royal Society, 2021-04-14) Jiménez-Zafra, Salud M.; Sáez-Castillo, Antonio J.; Conde-Sánchez, Antonio; Martín-Valdivia, M. Teresa
Virality on Twitter is catching the attention of researchers, trying to identify factors which increase or decrease the probability of retweeting. We study how terms expressing sentiments affect retweeting frequencies by means of a regression model on the number of retweets, which is specially accurate to deal with virality. We focus on the Spanish political situation during the pseudo-referendum held in Catalonia on 1 October 2017. We have found that the use of negativity in a tweet increases the probability of retweeting and that iSOL lexicon is the one that better determines the relationship between polarity and virality.
Hybrid-attack-resistant distributed state estimation for nonlinear complex networks with random coupling strength and sensor delays
(Elsevier, 2024-06-21) Lei, Bingxin; Hu, Jun; Caballero-Águila, Raquel; Chen, Cai
In this paper, a recursive distributed hybrid-attack-resistant state estimation (SE) scheme is proposed for a class of time-varying nonlinear complex networks (NCNs) subject to random coupling strength (RCS) and random sensor delays (RSDs) under hybrid attacks. A hybrid-attack model is considered to characterize the random occurrence of denial-of-service (DoS) attacks and deception attacks. The objective of the problem to be solved is to develop a recursive distributed estimation method such that, in the presence of RCS, RSDs and hybrid attacks, a locally optimized upper bound (UB) on the estimation error covariance (EEC) is ensured. By employing the mathematical induction method, a UB is firstly derived on the EEC. Subsequently, the obtained UB is minimized by appropriately designing the estimator gain (EG). Furthermore, a sufficient criterion guaranteeing the exponential boundedness (EB) of SE error is elaborated in the mean square sense (MSS). Finally, simulation experiments with localization applications of multiple mobile indoor robots are conducted to illustrate the applicability of the proposed SE scheme.
Mixed Static-Dynamic Protocol-Based Tobit Recursive Filtering for Stochastic Nonlinear Systems Against Random False Data Injection Attacks
(2024-04-22) Hu, Jun; Yang, Shuo; Caballero-Águila, Raquel; Dong, Hongli; Wu, Boying
In this paper, the Tobit recursive filtering (TRF) issue is discussed for a class of time-varying stochastic nonlinear systems (SNSs) with censored measurements and random false data injection attacks (FDIAs) under the mixed static-dynamic protocol. The censored measurements considered are depicted by the Tobit Type I model and the phenomenon of the random FDIAs involved is governed by a set of Bernoulli random variables. Additionally, in order to reduce the communication burden and improve the data utilization efficiency, the mixed static-dynamic protocol is elaborately adopted to schedule the signal transmission, which is managed by the time-triggered and event-triggered rules to further increase the flexibility of the data scheduling. The main goal of this paper is to present a new TRF approach such that, in the presence of censored measurements, mixed static-dynamic protocol and random FDIAs, a minimized upper bound of the filtering error covariance (FEC) can be obtained. Moreover, a sufficient criterion from the theoretical analysis perspective is established to guarantee the desired uniform boundedness of the filtering error in the mean-square sense (MSS). Finally, some experiments with comparisons applicable for three-wheeled Ackerman turning model are conducted to show the applicability and advantages of newly proposed TRF scheme.
Proper Adaptive Filtering in Four-Dimensional Cayley-Dickson Algebras
(Elsevier, 2023) Ruiz Molina, Juan Carlos; Navarro Moreno, Jesús; Fernández Alcalá, Rosa Mª; Jiménez López, José D.
A family of hypercomplex algebras in four dimensions (4D) is proposed to devise adaptive filters. Such a family, called $\beta$-quaternions, has multiplication rules for the complex units that depend on a parameter $\beta$, and this family contains, as particular cases, both standard Hamilton quaternions and split quaternions. In this framework, two notions of properness for random vectors are introduced and their implications on the statistical processing involved are analyzed. Then, statistical tests to check properness in practice and a method to select the best algebra where the properness conditions could hold are provided. Also, proper adaptive filters are suggested and row and column updating problems are studied. The main advantage of the techniques proposed compared with the standard ones is that a notable reduction in the computational burden is achieved. Finally, simulation examples validate the proper adaptive filters and demonstrate that our scheme performs better than the traditional quaternion domain.
Quadratic estimation for stochastic systems in the presence of random parameter matrices, time-correlated additive noise and deception attacks
(Elsevier, 2023-08-23) Caballero-Águila, Raquel; Linares-Pérez, Josefa
Networked systems usually face different random uncertainties that make the performance of the least-squares (LS) linear filter decline significantly. For this reason, great attention has been paid to the search for other kinds of suboptimal estimators. Among them, the LS quadratic estimation approach has attracted considerable interest in the scientific community for its balance between computational complexity and estimation accuracy. When it comes to stochastic systems subject to different random uncertainties and deception attacks, the quadratic estimator design has not been deeply studied. In this paper, using covariance information, the LS quadratic filtering and fixed-point smoothing problems are addressed under the assumption that the measurements are perturbed by a time-correlated additive noise, as well as affected by random parameter matrices and exposed to random deception attacks. The use of random parameter matrices covers a wide range of common uncertainties and random failures, thus better reflecting the engineering reality. The signal and observation vectors are augmented by stacking the original vectors with their second-order Kronecker powers; then, the linear estimator of the original signal based on the augmented observations provides the required quadratic estimator. A simulation example illustrates the superiority of the proposed quadratic estimators over the conventional linear ones and the effect of the deception attacks on the estimation performance.
Semi-Widely Linear Estimation Algorithms of Quaternion Signals with Missing Observations and Correlated Noises
(Elsevier, 2020) Ruiz Molina, Juan Carlos; Navarro Moreno, Jesús; Fernández Alcalá, Rosa Mª; Jiménez López, José D.
The paper deals with the estimation problem of a discrete-time vectorial quaternion signal which is observed through a linear dynamic system with intermittent observations and autocorrelated and cross-correlated noises. Under $\mathbb{C}^{\eta}$-properness conditions, a semi-widely linear processing is considered to provide filtering, fixed-point and fixed-lag smoothing algorithms for estimating the quaternion state. The proposed solutions give a substantial reduction in computational burden in relation to the widely linear estimation techniques, this benefit being impossible to be fully captured by using a real number framework. The feasibility and performance of the aforementioned algorithms are illustrated by means of simulation examples.
Study of the synergistic impact of Fe3O4, Na2CO3 and organic C on kaolin-based lightweight aggregates by a DOE (Mixture Experiments) approach
(Elsevier, 2023-11-03) Moreno-Maroto, José Manuel; Cobo-Ceacero, Carlos Javier; Martínez-Rodríguez, Ana María; Conde-Sánchez, Antonio; González-Corrochano, Beatriz; Alonso-Azcárate, Jacinto; Uceda, Manuel; López-García, Ana Belén; Martínez-García, Carmen; Cotes, Teresa
The compositional synergies involved in the thermal formation of lightweight aggregates (LWAs) have been investigated through four pure phases: non-expansive kaolin (K); cork powder (C); sodium carbonate (N) and magnetite-Fe3O4 (M). Mixture Experiments has been applied for formulation, modeling and optimization. LWAs have been manufactured from 36 starting mixtures and the main technological properties have been characterized: bloating index (BI), particle density (ρrd), water absorption (WA24) and crushing strength (S). Maximum BI and WA24 together with minimum density are associated with the addition of a significant amount of iron phase in combination with small proportions of organic carbon (Optimal [BI > 60%]: 56.0% K + 40.2% M + 3.9% C + 0% N), while S increases antagonistically with expansion. Iron reduction by incomplete combustion of C appears to be critical in pore formation and concomitant bloating. N has enhanced the sphericity of the expanded specimens. The contrast between experimental and estimated data has shown that the models have generally performed very well.
Tessarine signal processing under the T -properness condition
(Elsevier, 2020-09) Navarro-Moreno, Jesús; Fernández-Alcalá, Rosa María; Jiménez-López, José Domingo; Ruiz-Molina, Juan Carlos
The paper analyzes the processing of 4D commutative hypercomplex or tessarine signals under properness conditions. Firstly, the concept of T-properness is introduced and a procedure to test experimentally whether a tessarine random signal is proper or not is proposed. Then, for the class of T-proper signals, the linear minimum mean square error estimation problem is addressed. In this regard, it should be highlighted that although the tessarine algebra is not a Hilbert space, a metric which guarantees the existence and unicity of the optimal estimator is defined. Moreover, the equivalence, under T-properness conditions, between the optimal estimator based on a tessarine widely linear processing and the one based on a tessarine strictly linear (TSL) processing is also shown, attaining thus a notable reduction in computational burden. Finally, two T-proper models, a TSL state-space model, and a TSL stationary model, from which the optimal estimator can be recursively obtained are considered. In both cases, simulated examples are developed where the superiority of TSL processing over the counterparts in the quaternion domain is exhibited.

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