Penalized functional spatial regression

This paper is focus on spatial functional variables whose observa- tions are realizations of a spatio-temporal functional process. In this context, a new smoothing method for functional data presenting spa- tial dependence is proposed. This approach is based on a P-spline estimation of a functional spatial regression model. As alternative to other geostatistical smoothing methods (kriging and kernel smooth- ing, among others), the proposed P-spline approach can be used to estimate the functional form of a set of sample paths observed only at a finite set of time points, and also to predict the corresponding func- tional variable at a new location within the plane of study. In order to test the good performance of the proposed method, two simulation studies and an application with real data will be developed and the results will be compared with functional kriging.Financial support from the project P11-FQM-8068 from Consejería de Innovación, Ciencia y Empresa. Junta de Andalucía, Spain and the projects MTM2013-47929-P and MTM 2011-28285-C02-C2 from Secretaría de Estado Investigación, Desarrollo e Innovación, Ministerio de Economía y Competitividad, Spain

Group linear algorithm with sparse principal decomposition: a variable selection and clustering method for generalized linear models

[EN] This paper introduces the Group Linear Algorithm with Sparse Principal decomposition, an algorithm for supervised variable selection and clustering. Our approach extends the Sparse Group Lasso regularization to calculate clusters as part of the model fit. Therefore, unlike Sparse Group Lasso, our idea does not require prior specification of clusters between variables. To determine the clusters, we solve a particular case of sparse Singular Value Decomposition, with a regularization term that follows naturally from the Group Lasso penalty. Moreover, this paper proposes a unified implementation to deal with, but not limited to, linear regression, logistic regression, and proportional hazards models with right-censoring. Our methodology is evaluated using both biological and simulated data, and details of the implementation in R and hyperparameter search are discussed.Laria, JC.; Aguilera-Morillo, MC.; Lillo, RE. (2022). 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Adaptive sparse group LASSO in quantile regression

[EN] This paper studies the introduction of sparse group LASSO (SGL) to the quantile regression framework. Additionally, a more flexible version, an adaptive SGL is proposed based on the adaptive idea, this is, the usage of adaptive weights in the penalization. Adaptive estimators are usually focused on the study of the oracle property under asymptotic and double asymptotic frameworks. A key step on the demonstration of this property is to consider adaptive weights based on a initial root n-consistent estimator. In practice this implies the usage of a non penalized estimator that limits the adaptive solutions to low dimensional scenarios. In this work, several solutions, based on dimension reduction techniques PCA and PLS, are studied for the calculation of these weights in high dimensional frameworks. The benefits of this proposal are studied both in synthetic and real datasets.We appreciate the work of the referees that has contributed to substantially improve the scientific contributions of this work. In this research we have made use of Uranus, a supercomputer cluster located at University Carlos III of Madrid and funded jointly by EU-FEDER funds and by the Spanish Government via the National Projects No. UNC313-4E-2361, No. ENE2009-12213- C03-03, No. ENE2012-33219 and No. ENE2015-68265-P. This research was partially supported by research grants and Project ECO2015-66593-P from Ministerio de Economia, Industria y Competitividad, Project MTM2017-88708-P from Ministerio de Economia y Competitividad, FEDER funds and Project IJCI-2017-34038 from Agencia Estatal de Investigacion, Ministerio de Ciencia, Innovacion y Universidades.Mendez-Civieta, A.; Aguilera-Morillo, MC.; Lillo, RE. (2021). Adaptive sparse group LASSO in quantile regression. 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Penalized function-on-function partial leastsquares regression

This paper deals with the "function-on-function'" or "fully functional" linear regression problem. We address the problem by proposing a novel penalized Function-on-Function Partial Least-Squares (pFFPLS) approach that imposes smoothness on the PLS weights. Our proposal introduces an appropriate finite-dimensional functional space with an associated set of bases on which to represent the data and controls smoothness with a roughness penalty operator. Penalizing the PLS weights imposes smoothness on the resulting coefficient function, improving its interpretability. In a simulation study, we demonstrate the advantages of pFFPLS compared to non-penalized FFPLS. Our comparisons indicate a higher accuracy of pFFPLS when predicting the response and estimating the true coefficient function from which the data were generated. We also illustrate the advantages of our proposal with two case studies involving two well-known datasets from the functional data analysis literature. In the first one, we predict log precipitation curves from the yearly temperature profiles recorded in 35 weather stations in Canada. In the second case study, we predict the hip angle profiles during a gait cycle of children from their corresponding knee angle profiles

Linear-Phase-Type probability modelling of functional PCA with applications to resistive memories

[EN] Functional principal component analysis (FPCA) based on Karhunen-Loeve (K-L) expansion allows to describe the stochastic evolution of the main characteristics associated to multiple systems and devices. Identifying the probability distribution of the principal component scores is fundamental to characterize the whole process. The aim of this work is to consider a family of statistical distributions that could be accurately adjusted to a previous transformation. Then, a new class of distributions, the linear-phase-type, is introduced to model the principal components. This class is studied in detail in order to prove, through the K-L expansion, that certain linear transformations of the process at each time point are phase-type distributed. This way, the one-dimensional distributions of the process are in the same linear-phase-type class. Finally, an application to model the reset process associated with resistive memories is developed and explained. (C) 2020 Published by Elsevier B.V. on behalf of International Association for Mathematics and Computers in Simulation (IMACS).We would like to thank F. Campabadal and M.B. Gonzalez from the IMB-CNM (CSIC) in Barcelona for fabricating and providing the experimental measurements of the devices employed here. We acknowledge the support of the Spanish Ministry of Science, Innovation and Universities under projects TEC2017-84321-C4-3-R, MTM201788708-P, IJCI-2017-34038 (also supported by the FEDER, Spain program) and the PhD grant, Spain (FPU18/01779) awarded to Christian Acal. This work has made use of the Spanish ICTS Network MICRONANOFABSRuiz-Castro, JE.; Acal, C.; Aguilera, AM.; Aguilera-Morillo, MC.; Roldán, JB. (2021). Linear-Phase-Type probability modelling of functional PCA with applications to resistive memories. Mathematics and Computers in Simulation. 186:71-79. https://doi.org/10.1016/j.matcom.2020.07.006717918

Functional modeling of high-dimensional data: a Manifold Learning approach

This article belongs to the Special Issue Methodological and Applied Contributions on Stochastic Modelling and ForecastingThis paper introduces stringing via Manifold Learning (ML-stringing), an alternative to the original stringing based on Unidimensional Scaling (UDS). Our proposal is framed within a wider class of methods that map high-dimensional observations to the infinite space of functions,allowing the use of Functional Data Analysis (FDA). Stringing handles general high-dimensional data as scrambled realizations of an unknown stochastic process. Therefore, the essential feature of the method is a rearrangement of the observed values. Motivated by the linear nature of UDS and the increasing number of applications to biosciences (e.g., functional modeling of gene expression arrays and single nucleotide polymorphisms, or the classification of neuroimages) we aim to recover more complex relations between predictors through ML. In simulation studies, it is shown that MLstringing achieves higher-quality orderings and that, in general, this leads to improvements in the functional representation and modeling of the data. The versatility of our method is also illustrated with an application to a colon cancer study that deals with high-dimensional gene expression arrays.This paper shows that ML-stringing is a feasible alternative to the UDS-based version. Also, it opens a window to new contributions to the field of FDA and the study of high-dimensional data.This research was funded in part by Ministerio de Ciencia, Innovación y Universidades grant numbers PID2019-104901RB-I00 and MTM2017-88708-P

Stepwise selection of functional covariates in forecasting peak levels of olive pollen

High levels of airborne olive pollen represent a problem for a large proportion of the population because of the many allergies it causes. Many attempts have been made to forecast the concentration of airborne olive pollen, using methods such as time series, linear regression, neural networks, a combination of fuzzy systems and neural networks, and functional models. This paper presents a functional logistic regression model used to study the relationship between olive pollen concentration and different climatic factors, and on this basis to predict the probability of high (and possibly extreme) levels of airborne pollen, selecting the best subset of functional climatic variables by means of a stepwise method based on the conditional likelihood ratio test.Projects MTM2010-20502 from Dirección General de Investigación del MEC, Spain and FQM-307 from Consejería de Innovación, Ciencia y Empresa de la Junta de Andalucía Spai

Dendritic cell deficiencies persist seven months after SARS-CoV-2 infection

Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV)-2 infection induces an exacerbated inflammation driven by innate immunity components. Dendritic cells (DCs) play a key role in the defense against viral infections, for instance plasmacytoid DCs (pDCs), have the capacity to produce vast amounts of interferon-alpha (IFN-α). In COVID-19 there is a deficit in DC numbers and IFN-α production, which has been associated with disease severity. In this work, we described that in addition to the DC deficiency, several DC activation and homing markers were altered in acute COVID-19 patients, which were associated with multiple inflammatory markers. Remarkably, previously hospitalized and nonhospitalized patients remained with decreased numbers of CD1c+ myeloid DCs and pDCs seven months after SARS-CoV-2 infection. Moreover, the expression of DC markers such as CD86 and CD4 were only restored in previously nonhospitalized patients, while no restoration of integrin β7 and indoleamine 2,3-dyoxigenase (IDO) levels were observed. These findings contribute to a better understanding of the immunological sequelae of COVID-19

SARS-CoV-2 viral load in nasopharyngeal swabs is not an independent predictor of unfavorable outcome

The aim was to assess the ability of nasopharyngeal SARS-CoV-2 viral load at first patient’s hospital evaluation to predict unfavorable outcomes. We conducted a prospective cohort study including 321 adult patients with confirmed COVID-19 through RT-PCR in nasopharyngeal swabs. Quantitative Synthetic SARS-CoV-2 RNA cycle threshold values were used to calculate the viral load in log10 copies/mL. Disease severity at the end of follow up was categorized into mild, moderate, and severe. Primary endpoint was a composite of intensive care unit (ICU) admission and/or death (n = 85, 26.4%). Univariable and multivariable logistic regression analyses were performed. Nasopharyngeal SARS-CoV-2 viral load over the second quartile (≥ 7.35 log10 copies/mL, p = 0.003) and second tertile (≥ 8.27 log10 copies/mL, p = 0.01) were associated to unfavorable outcome in the unadjusted logistic regression analysis. However, in the final multivariable analysis, viral load was not independently associated with an unfavorable outcome. Five predictors were independently associated with increased odds of ICU admission and/or death: age ≥ 70 years, SpO2, neutrophils > 7.5 × 103/µL, lactate dehydrogenase ≥ 300 U/L, and C-reactive protein ≥ 100 mg/L. In summary, nasopharyngeal SARS-CoV-2 viral load on admission is generally high in patients with COVID-19, regardless of illness severity, but it cannot be used as an independent predictor of unfavorable clinical outcome