Kernel Inverse Regression for spatial random fields

In this paper, we propose a dimension reduction model for spatially dependent variables. Namely, we investigate an extension of the \emph{inverse regression} method under strong mixing condition. This method is based on estimation of the matrix of covariance of the expectation of the explanatory given the dependent variable, called the \emph{inverse regression}. Then, we study, under strong mixing condition, the weak and strong consistency of this estimate, using a kernel estimate of the \emph{inverse regression}. We provide the asymptotic behaviour of this estimate. A spatial predictor based on this dimension reduction approach is also proposed. This latter appears as an alternative to the spatial non-parametric predictor

Régression et prédiction non-paramétrique spatiale

International audienceNous nous intéressons à l'estimation de la fonction de régression r(x)=E\left(Y_{\mathbfu}|X_{\mathbfu}=x\right) à partir d'observations d'un processus \left\{ Z_{\mathbfi}=\left(X_{\mathbfi},\ Y_{\mathbfi}\right),\,\mathbfi\in\mathbbZ^N\right\}. On suppose que les variables Z_{\mathbfi}$sont de même distribution que$Z=(X,Y)$, où$Y$est une variable réelle, intégrable et$X$un vecteur aléatoire à valeurs dans un espace séparable$\mathcalE$muni (éventuellement de dimension infinie). Dans ce travail, la convergence nos estimateurs est étudiée sous conditions de mélange à partir d'observations dans une région rectangulaire de$\mathbbZ^N$. Nous illustrerons nos résultats par des simulations. L'application de nos méthodes à la prédiction spatiale sera également abordée

KERNEL SPATIAL DENSITY ESTIMATION IN INFINITE DIMENSION SPACE

In this paper, we propose a nonparametric estimation of the spatial density of a functional stationary random field. This later is with values in some infinite dimensional space and admitted a density with respect to some reference measure. The weak and strong consistencies of the estimator are shown and rates of convergence are given. Special attention is paid to the links between the probabilities of small balls in the concerned infinite dimensional space and the rates of convergence. The practical use and the behavior of the estimator are illustrated through some simulations and a real data application

KERNEL REGRESSION ESTIMATION FOR SPATIAL FUNCTIONAL RANDOM VARIABLES

Given a spatial random process (Xi; Yi) 2 E R; i 2 ZN , we investigate a nonparametric estimate of the conditional expectation of the real random variable Yi given the functional random field Xi valued in a semi-metric space E. The weak and strong consistencies of the estimate are shown and almost sure rates of convergence are given. Special attention is paid to apply the regression estimate introduced to spatial prediction problems

Bayesian spatio-temporal kriging with misspecified black-box

We propose a new algorithm for spatio-temporal prediction. At a given time t, we use a Bayesian kriging model for spatial prediction. The temporal evolution from t to t + 1 is given by a deterministic black-box which can be a complex numerical code or a partial differential equation. As often in practice, the black-box is misspecified, in the sense that its parameters are imprecisely known or may be varying randomly over time. At time t, we use the black-box to obtain a rough prediction at time t + 1. When new data are available, the black-box is used to estimate the hyperparameters of the Bayesian kriging at time t + 1 by using Monte Carlo methods. Through a numerical application, we show that our method improves the values predicted by the black-box only

International Society of Human and Animal Mycology (ISHAM)-ITS reference DNA barcoding database - the quality controlled standard tool for routine identification of human and animal pathogenic fungi

Human and animal fungal pathogens are a growing threat worldwide leading to emerging infections and creating new risks for established ones. There is a growing need for a rapid and accurate identification of pathogens to enable early diagnosis and targeted antifungal therapy. Morphological and biochemical identification methods are time-consuming and require trained experts. Alternatively, molecular methods, such as DNA barcoding, a powerful and easy tool for rapid monophasic identification, offer a practical approach for species identification and less demanding in terms of taxonomical expertise. However, its wide-spread use is still limited by a lack of quality-controlled reference databases and the evolving recognition and definition of new fungal species/complexes. An international consortium of medical mycology laboratories was formed aiming to establish a quality controlled ITS database under the umbrella of the ISHAM working group on "DNA barcoding of human and animal pathogenic fungi." A new database, containing 2800 ITS sequences representing 421 fungal species, providing the medical community with a freely accessible tool at http://www.isham.org and http://its.mycologylab.org/ to rapidly and reliably identify most agents of mycoses, was established. The generated sequences included in the new database were used to evaluate the variation and overall utility of the ITS region for the identification of pathogenic fungi at intra-and interspecies level. The average intraspecies variation ranged from 0 to 2.25%. This highlighted selected pathogenic fungal species, such as the dermatophytes and emerging yeast, for which additional molecular methods/genetic markers are required for their reliable identification from clinical and veterinary specimens.This study was supported by an National Health and Medical Research Council of Australia (NH&MRC) grant [#APP1031952] to W Meyer, S Chen, V Robert, and D Ellis; CNPq [350338/2000-0] and FAPERJ [E-26/103.157/2011] grants to RM Zancope-Oliveira; CNPq [308011/2010-4] and FAPESP [2007/08575-1] Fundacao de Amparo Pesquisa do Estado de So Paulo (FAPESP) grants to AL Colombo; PEst-OE/BIA/UI4050/2014 from Fundacao para a Ciencia e Tecnologia (FCT) to C Pais; the Belgian Science Policy Office (Belspo) to BCCM/IHEM; the MEXBOL program of CONACyT-Mexico, [ref. number: 1228961 to ML Taylor and [122481] to C Toriello; the Institut Pasteur and Institut de Veil le Sanitaire to F Dromer and D Garcia-Hermoso; and the grants from the Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq) and the Fundacao de Amparo a Pesquisa do Estado de Goias (FAPEG) to CM de Almeida Soares and JA Parente Rocha. I Arthur would like to thank G Cherian, A Higgins and the staff of the Molecular Diagnostics Laboratory, Division of Microbiology and Infectious Diseases, Path West, QEII Medial Centre. Dromer would like to thank for the technical help of the sequencing facility and specifically that of I, Diancourt, A-S Delannoy-Vieillard, J-M Thiberge (Genotyping of Pathogens and Public Health, Institut Pasteur). RM Zancope-Oliveira would like to thank the Genomic/DNA Sequencing Platform at Fundacao Oswaldo Cruz-PDTIS/FIOCRUZ [RPT01A], Brazil for the sequencing. B Robbertse and CL Schoch acknowledge support from the Intramural Research Program of the NIH, National Library of Medicine. T Sorrell's work is funded by the NH&MRC of Australia; she is a Sydney Medical School Foundation Fellow.info:eu-repo/semantics/publishedVersio

Un modèle semi-paramétrique pour variables fonctionnelles : la régression inverse fonctionnelle

TOULOUSE3-BU Sciences (315552104) / SudocSudocFranceF

Kernel spatial density estimation in infinite dimension space

International audienceIn this paper, we propose a nonparametric method to estimate the spatial density of a functional stationary random field. This latter is with values in some infinite dimensional normed space and admitted a density with respect to some reference measure. We study both the weak and strong consistencies of the considered estimator and also give some rates of convergence. Special attention is paid to the links between the probabilities of small balls and the rates of convergence of the estimator. The practical use and the behavior of the estimator are illustrated through some simulations and a real data application. Copyright Springer-Verlag 201

Spatial kernel density estimation for functional random variables

International audienc