Prediction of air pollutants PM10 by ARBX(1) processes

This work adopts a Banach-valued time series framework for component-wise estimation and prediction, from temporal correlated functional data, in presence of exogenous variables. The strong-consistency of the proposed functional estimator and associated plug-in predictor is formulated. The simulation study undertaken illustrates their large-sample size properties. Air pollutants PM10 curve forecasting, in the Haute-Normandie region (France), is addressed by implementation of the functional time series approach presente

A goodness-of-fit test for the functional linear model with functional response

The functional linear model with functional response (FLMFR) is one of the most fundamental models to assess the relation between two functional random variables. In this article, we propose a novel goodness-of-fit test for the FLMFR against a general, unspecified, alternative. The test statistic is formulated in terms of a Cramér–von Mises norm over a doubly projected empirical process which, using geometrical arguments, yields an easy-to-compute weighted quadratic norm. A resampling procedure calibrates the test through a wild bootstrap on the residuals and the use of convenient computational procedures. As a sideways contribution, and since the statistic requires a reliable estimator of the FLMFR, we discuss and compare several regularized estimators, providing a new one specifically convenient for our test. The finite sample behavior of the test is illustrated via a simulation study. Also, the new proposal is compared with previous significance tests. Two novel real data sets illustrate the application of the new test.Spanish Ministry of Economy and Competitiveness, IJCI-2017-32005; MTM2016-76969-P; PGC2018-097284-B-I00; PGC2018-099549-B-I00; Government of the Principality of Asturias (Severo Ochoa Program, grant PA-20-PF-BP19-053)

A goodness-of-fit test for the functional linear model with functional response

The Functional Linear Model with Functional Response (FLMFR) is one of the most fundamental models to assess the relation between two functional random variables. In this paper, we propose a novel goodness-of-fit test for the FLMFR against a general, unspecified, alternative. The test statistic is formulated in terms of a Cram\'er-von Mises norm over a doubly-projected empirical process which, using geometrical arguments, yields an easy-to-compute weighted quadratic norm. A resampling procedure calibrates the test through a wild bootstrap on the residuals and the use of convenient computational procedures. As a sideways contribution, and since the statistic requires a reliable estimator of the FLMFR, we discuss and compare several regularized estimators, providing a new one specifically convenient for our test. The finite sample behavior of the test is illustrated via a simulation study. Also, the new proposal is compared with previous significance tests. Two novel real datasets illustrate the application of the new test.Comment: 24 pages, 2 figures, 10 tables. Suplementary material: 2 pages, 1 figur

Stressors in nursing students during their clinical placements: a systematic review study

[ES] Los estudiantes de enfermería perciben niveles de estrés superiores a otros universitarios, al estar sometidos a tensiones tanto a nivel académico como clínico. Objetivo: Identificar los factores generadores de estrés en los estudiantes de enfermería durante la realización de las prácticas clínicas. Método: Se llevó a cabo una revisión sistemática de los artículos publicados en las bases de datos PubMed, Web of Science, Scopus, CUIDEN, SciELO; se seleccionaron aquellos artículos que cumplían los criterios de inclusión. Resultado: De 147 artículos, solo 8 estudios fueron incluidos por ser potencialmente relevantes. Los estudiantes de enfermería presentan estrés moderado en sus prácticas clínicas, siendo mayor en las chicas y en los primeros cursos. Discusión y conclusión: Las principales situaciones de estrés fueron falta de competencia, incertidumbre, contacto con el sufrimiento, relación con los profesores e implicación emocional. Se deberían desarrollar programas educativos para prevenir el estrés[EN] Nursing students perceive higher levels of stress than other university students, being subjected to both academic and clinical stress. Objective: To identify the factors that generate stress in nursing students during clinical practice. Method: A systematic review of articles published in the databases PubMed, Web of Science, Scopus, CUIDEN, SciELO was carried out; those articles that met the inclusion criteria were selected. Result: Out of 147 articles, only 8 studies were included as potentially relevant. Nursing students present moderate stress in their clinical practice, being higher in girls and in the first courses. Discussion and conclusion: The main stressful situations were lack of competence, uncertainty, contact with suffering, relationship with teachers and emotional involvement. Educational programs should be developed to prevent stress.S

Mycobacterium caprae Infection in Livestock and Wildlife, Spain

Mycobacterium caprae is a pathogen that can infect animals and humans. To better understand the epidemiology of M. caprae, we spoligotyped 791 animal isolates. Results suggest infection is widespread in Spain, affecting 6 domestic and wild animal species. The epidemiology is driven by infections in caprids, although the organism has emerged in cattle

Treatment with tocilizumab or corticosteroids for COVID-19 patients with hyperinflammatory state: a multicentre cohort study (SAM-COVID-19)

Objectives: The objective of this study was to estimate the association between tocilizumab or corticosteroids and the risk of intubation or death in patients with coronavirus disease 19 (COVID-19) with a hyperinflammatory state according to clinical and laboratory parameters. Methods: A cohort study was performed in 60 Spanish hospitals including 778 patients with COVID-19 and clinical and laboratory data indicative of a hyperinflammatory state. Treatment was mainly with tocilizumab, an intermediate-high dose of corticosteroids (IHDC), a pulse dose of corticosteroids (PDC), combination therapy, or no treatment. Primary outcome was intubation or death; follow-up was 21 days. Propensity score-adjusted estimations using Cox regression (logistic regression if needed) were calculated. Propensity scores were used as confounders, matching variables and for the inverse probability of treatment weights (IPTWs). Results: In all, 88, 117, 78 and 151 patients treated with tocilizumab, IHDC, PDC, and combination therapy, respectively, were compared with 344 untreated patients. The primary endpoint occurred in 10 (11.4%), 27 (23.1%), 12 (15.4%), 40 (25.6%) and 69 (21.1%), respectively. The IPTW-based hazard ratios (odds ratio for combination therapy) for the primary endpoint were 0.32 (95%CI 0.22-0.47; p < 0.001) for tocilizumab, 0.82 (0.71-1.30; p 0.82) for IHDC, 0.61 (0.43-0.86; p 0.006) for PDC, and 1.17 (0.86-1.58; p 0.30) for combination therapy. Other applications of the propensity score provided similar results, but were not significant for PDC. Tocilizumab was also associated with lower hazard of death alone in IPTW analysis (0.07; 0.02-0.17; p < 0.001). Conclusions: Tocilizumab might be useful in COVID-19 patients with a hyperinflammatory state and should be prioritized for randomized trials in this situatio

Inference on linear processes in Hilbert and Banach spaces: statistical analysis of high-dimensional data

Esta tesis proporciona nuevos resultados en el contexto de la estimación y predicción funcional, a partir de modelos autorregresivos Hilbertianos, o bien, con valores en espacios de Banach separables. El objetivo fundamental es proporcionar herramientas adecuadas para modelizar relaciones lineales entre variables aleatorias funcionales, que dependen de un índice temporal. Se ha adoptado un enfoque paramétrico, en la estimación funcional, basado en proyectar sobre bases ortonormales adecuadas. Los resultados derivados, sobre propiedades asintóticas de los estimadores considerados, se aplican al contexto de la regresión lineal funcional, con errores correlados en el tiempo, y con valores funcionales en espacios de Hilbert separables. En particular, se considera un análisis funcional de la varianza para dichos modelos. Adicionalmente, se introduce un enfoque Bayesiano en la derivación de la aproximación considerada, componente a componente, para el operador de autocorrelación, bajo condiciones menos restrictivas. El enfoque no paramétrico se contempla en la clasificación de datos funcionales con soporte espacial.This PhD thesis focuses on statistical estimation and prediction from temporal correlated functional data. We adopt the functional time series framework, considering, in particular, autoregressive processes in Hilbert and Banach spaces (ARH(1) and ARB(1) processes). Our primary objective is the statistical estimation of the conditional mean, from temporal correlated data, considering linear models in a parametric framework. That is the case, for example, of the estimation of the functional response in linear regression, with functional regressors and correlated errors, lying in Hilbert or Banach spaces. Some extensions to the Bayesian framework are derived as well. Nonparametric classification is also considered, in the special case of spatially supported uncorrelated functional data.Tesis Univ. Granada.Programa Oficial de Doctorado en Estadística Matemática y AplicadaThis dissertation has been supported in part by projects MTM2012-32674 and MTM2015–71839–P (co-funded by Feder funds), of the DGI, MINECO, Spain

Prediction of air pollutants PM10 by ARBX(1) processes

This work adopts a Banach-valued time series framework for component-wise estimation and prediction, from temporal correlated functional data, in presence of exogenous variables. The strong-consistency of the proposed functional estimator and associated plug-in predictor is formulated. The simulation study undertaken illustrates their large-sample size properties. Air pollutants PM10 curve forecasting, in the Haute-Normandie region (France), is addressed by implementation of the functional time series approach presented.Depto. de Estadística y Ciencia de los DatosFac. de Estudios EstadísticosTRUEpu

Strongly consistent autoregressive predictors in abstract Banach spaces

https://ars-els-cdn-com.bucm.idm.oclc.org/content/image/1-s2.0-S0047259X17307248-mmc1.pdfThis work derives new results on strong consistent estimation and prediction for autoregressive processes of order 1 in a separable Banach space B. The consistency results are obtained for the component-wise estimator of the autocorrelation operator in the norm of the space L(B) of bounded linear operators on B. The strong consistency of the associated plug-in predictor then follows in the B-norm. A Gelfand triple is defined through the Hilbert space constructed in Kuelbs’ lemma (Kuelbs, 1970). A Hilbert–Schmidt embedding introduces the Reproducing Kernel Hilbert space (RKHS), generated by the autocovariance operator, into the Hilbert space conforming the Rigged Hilbert space structure. This paper extends the work of Bosq (2000) and Labbas and Mourid (2002).Dirección General de Investigación (DGI). Ministerio de Economía y Competitividad (MINECO). EspañaDepto. de Estadística y Ciencia de los DatosFac. de Estudios EstadísticosTRUEpu

A note on strong-consistency of componentwise ARH(1) predictors

New results on strong-consistency in the trace operator norm are obtained, in the parameter estimation of an autoregressive Hilbertian process of order one (ARH(1) process). Additionally, a strongly-consistent diagonal componentwise estimator of the autocorrelation operator is derived, based on its empirical singular value decomposition.Depto. de Estadística e Investigación OperativaFac. de Estudios EstadísticosTRUEpu