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7 results on '"Camille Ternynck"'

1. Effects of Immunoglobulins G From Systemic Sclerosis Patients in Normal Dermal Fibroblasts: A Multi-Omics Study

Author

Aurélien Chepy, Solange Vivier, Fabrice Bray, Camille Ternynck, Jean-Pascal Meneboo, Martin Figeac, Alexandre Filiot, Lucile Guilbert, Manel Jendoubi, Christian Rolando, David Launay, Sylvain Dubucquoi, Guillemette Marot, Vincent Sobanski, Service de médecine interne [Lille], Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Institute for Translational Research in Inflammation - U 1286 (INFINITE (Ex-Liric)), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Lille Inflammation Research International Center - U 995 (LIRIC), Institut Pasteur de Lille, Réseau International des Instituts Pasteur (RIIP)-Réseau International des Instituts Pasteur (RIIP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Miniaturisation pour la Synthèse, l’Analyse et la Protéomique - UAR 3290 (MSAP), Université de Lille-Centre National de la Recherche Scientifique (CNRS), Evaluation des technologies de santé et des pratiques médicales - ULR 2694 (METRICS), Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Plateformes Lilloises en Biologie et Santé - UAR 2014 - US 41 (PLBS), Réseau International des Instituts Pasteur (RIIP)-Réseau International des Instituts Pasteur (RIIP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-Centre National de la Recherche Scientifique (CNRS), Institut d'Immunologie [CHRU Lille], Pôle de Biologie Pathologie Génétique [CHU Lille], Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), MOdel for Data Analysis and Learning (MODAL), Laboratoire Paul Painlevé (LPP), Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Evaluation des technologies de santé et des pratiques médicales - ULR 2694 (METRICS), Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-École polytechnique universitaire de Lille (Polytech Lille), Institut Universitaire de France (IUF), Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche (M.E.N.E.S.R.), Université de Lille, CHU Lille, Institute for Translational Research in Inflammation - U 1286 [INFINITE (Ex-Liric)], Institut de Recherche Translationnelle sur l'Inflammation (INFINITE) - U1286, Miniaturisation pour la Synthèse, l'Analyse et la Protéomique (MSAP) - USR 3290, Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS], Plateformes Lilloises en Biologie et Santé - UAR 2014 - US 41 [PLBS], Miniaturisation pour la Synthèse, l’Analyse et la Protéomique - UAR 3290 [MSAP], MOdel for Data Analysis and Learning [MODAL], METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694, and Institut Universitaire de France [IUF]

Subjects
systemic sclerosis, multi-omics analysis, autoantibodies, proteomics, transcriptomics, Scleroderma, Systemic, Proteome, [SDV]Life Sciences [q-bio], Immunology, Fibroblasts, Tandem Mass Spectrometry, Immunoglobulin G, Humans, Immunology and Allergy, Chromatography, Liquid
Abstract

International audience; Autoantibodies (Aabs) are frequent in systemic sclerosis (SSc). Although recognized as potent biomarkers, their pathogenic role is debated. This study explored the effect of purified immunoglobulin G (IgG) from SSc patients on protein and mRNA expression of dermal fibroblasts (FBs) using an innovative multi-omics approach. Dermal FBs were cultured in the presence of sera or purified IgG from patients with diffuse cutaneous SSc (dcSSc), limited cutaneous SSc or healthy controls (HCs). The FB proteome and transcriptome were explored using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) and microarray assays, respectively. Proteomic analysis identified 3,310 proteins. SSc sera and purified IgG induced singular protein profile patterns. These FB proteome changes depended on the Aab serotype, with a singular effect observed with purified IgG from anti-topoisomerase-I autoantibody (ATA) positive patients compared to HC or other SSc serotypes. IgG from ATA positive SSc patients induced enrichment in proteins involved in focal adhesion, cadherin binding, cytosolic part, or lytic vacuole. Multi-omics analysis was performed in two ways: first by restricting the analysis of the transcriptomic data to differentially expressed proteins; and secondly, by performing a global statistical analysis integrating proteomics and transcriptomics. Transcriptomic analysis distinguished 764 differentially expressed genes and revealed that IgG from dcSSc can induce extracellular matrix (ECM) remodeling changes in gene expression profiles in FB. Global statistical analysis integrating proteomics and transcriptomics confirmed that IgG from SSc can induce ECM remodeling and activate FB profiles. This effect depended on the serotype of the patient, suggesting that SSc Aab might play a pathogenic role in some SSc subsets.

Published
2022
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2. The French multicentre elevated bone mass study: prevalence and causes

Author

Isabelle Legroux-Gérot, Aurore Nottez, Eric Lespessailles, Christian Roux, Julien Paccou, Bernard Cortet, Patrice Fardellone, Camille Ternynck, Rose-Marie Javier, Pascal Guggenbuhl, Isabelle Henry-Desailly, François Robin, Sami Kolta, CHU Lille, Marrow Adiposity & Bone Lab - Adiposité Médullaire et Os - ULR 4490 (MABLab (ex-pmoi)), Université du Littoral Côte d'Opale (ULCO)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), CHU Strasbourg, CHU Amiens-Picardie, Evaluation des technologies de santé et des pratiques médicales - ULR 2694 (METRICS), Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Nutrition, Métabolismes et Cancer (NuMeCan), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), CHU Pontchaillou [Rennes], Imagerie Multimodale Multiéchelle et Modélisation du Tissu Osseux et articulaire (I3MTO), Université d'Orléans (UO), Centre de Recherche Épidémiologie et Statistique Sorbonne Paris Cité (CRESS (U1153 / UMR_A_1125 / UMR_S_1153)), Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), SFR (Societe Francaise de Rhumatologie), Hôpital de Hautepierre [Strasbourg], Service de Rhumatologie - AMIENS (AMIENS - Rhumato), Caractérisation du tissu osseux par imagerie : techniques et applications, Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional d'Orléans (CHR)-Université d'Orléans (UO), Centre Hospitalier Régional d'Orléans (CHR), Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Mécanismes physiopathologiques et conséquences des calcifications vasculaires - UR UPJV 7517 (MP3CV), Université de Picardie Jules Verne (UPJV)-CHU Amiens-Picardie, Conservatoire National des Arts et Métiers [CNAM] (CNAM), and HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)

Subjects
Adult, Male, 0301 basic medicine, musculoskeletal diseases, medicine.medical_specialty, Pediatrics, Endocrinology, Diabetes and Metabolism, [SDV]Life Sciences [q-bio], education, 030209 endocrinology & metabolism, Diseases of the musculoskeletal system, Gastroenterology, 03 medical and health sciences, Absorptiometry, Photon, 0302 clinical medicine, Degenerative disease, Bone Density, Internal medicine, Epidemiology, Osteoarthritis, Prevalence, Humans, Medicine, Orthopedics and Sports Medicine, Renal osteodystrophy, Bone-fat interactions, ComputingMilieux_MISCELLANEOUS, Retrospective Studies, Bone mineral, DXA, 0303 health sciences, Lumbar Vertebrae, business.industry, Retrospective cohort study, medicine.disease, Rheumatology, humanities, 3. Good health, RC925-935, 030301 anatomy & morphology, Hypoparathyroidism, [SDV.MHEP.RSOA]Life Sciences [q-bio]/Human health and pathology/Rhumatology and musculoskeletal system, Elevated bone mass, Osteopetrosis, Orthopedic surgery, Parathyroid-related disorders, Female, 030101 anatomy & morphology, business
Abstract

International audience; The purpose of this multicentric study was to evaluate the prevalence and causes of Elevated Bone Mass (EBM) in patients who underwent DXA scanning over a 10-year period. The prevalence of EBM was 1 in 100. The main causes of EBM were degenerative spine disorders and renal osteodystrophy. Introduction Reports of elevated bone mass (EBM) on routine dual energy X-Ray absorptiometry (DXA) scanning are not infrequent. However, epidemiological studies of EBM are few and definition thresholds are variable. The purpose of this French multicentric study was to evaluate the prevalence and causes of EBM in adult patients who underwent DXA scanning over a 10-year period. Methods This multicentric, retrospective study was conducted in six French regional bone centres. DXA databases were initially searched for individuals with a bone mineral density (BMD) Z-score >= +4 at any site in the lumbar spine or hip from April 1st, 2008 to April 30st, 2018. Results In all, 72,225 patients with at least one DXA scan were identified. Of these, 909 (322 men and 587 women) had a Z-score >= + 4, i.e. a prevalence of 1.26% [1.18-1.34%]. The DXA scan reports and imagery and medical records of the 909 EBM patients were reviewed and 936 causes were found. In 42 patients (4%), no cause could be determined due to unavailability of data. Artefactual causes of EBM were found in 752 patients (80%), in whom the predominant cause was degenerative disease of the spine (613 patients, 65%). Acquired causes of focal EBM-including Paget's disease (n = 7)-were found in 12 patients (1%), and acquired causes of generalized EBM-including renal osteodystrophy (n = 32), haematological disorders (n = 20) and hypoparathyroidism (n = 15)-in 84 patients (9%). Other causes were rare hereditary diseases and unknown EBM in 19 (2%) and 27 (3%) cases respectively. Conclusions The prevalence of EBM was approximately 1 in 100. These findings suggest that degenerative disease of the spine is the main cause of EBM, but that acquired or hereditary diseases are also causal factors.

Published
2021
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3. Change point detection of flood events using a functional data framework

Author

Taha B. M. J. Ouarda, Fateh Chebana, Sophie Dabo-Niang, Mohamed Ben Alaya, Camille Ternynck, Institut National de la Recherche Scientifique [Québec] (INRS), Evaluation des technologies de santé et des pratiques médicales - ULR 2694 (METRICS), Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), MOdel for Data Analysis and Learning (MODAL), Laboratoire Paul Painlevé (LPP), Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Evaluation des technologies de santé et des pratiques médicales - ULR 2694 (METRICS), Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-École polytechnique universitaire de Lille (Polytech Lille), and Laboratoire Paul Painlevé - UMR 8524 (LPP)

Subjects
010504 meteorology & atmospheric sciences, Computer science, 0208 environmental biotechnology, Functional approach, Change point detection, 02 engineering and technology, computer.software_genre, 01 natural sciences, Flood, Lead (geology), [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], 0105 earth and related environmental sciences, Water Science and Technology, Series (mathematics), Flood myth, Functional data analysis, Point method, 6. Clean water, 020801 environmental engineering, Current (stream), 13. Climate action, Data mining, Hydrology, computer, Change detection
Abstract

International audience; Change point detection methods have an important role in many hydrological and hydraulic studies of river basins. These methods are very useful to characterize changes in hydrological regimes and can, therefore, lead to better understanding changes in extreme flows behavior. Flood events are generally characterized by a finite number of characteristics that may not include the entire information available in a discharge time series. The aim of the current work is to present a new approach to detect changes in flood events based on a functional data analysis framework. The use of the functional approach allows taking into account the whole information contained in the discharge time series of flood events. The presented methodology is illustrated on a flood analysis case study, from the province of Quebec, Canada. Obtained results using the proposed approach are consistent with those obtained using a traditional change point method, and demonstrate the capability of the functional framework to simultaneously consider several flood features and, therefore, presenting a comprehensive way for a better exploitation of the information contained in a discharge time series.

Published
2020
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4. Normative health-related fitness values for French children: The Diagnoform Programme

Author

Camille Ternynck, Thibault Deschamps, Jérémy Vanhelst, Hervé Ovigneur, Lille Inflammation Research International Center - U 995 (LIRIC), Institut Pasteur de Lille, Réseau International des Instituts Pasteur (RIIP)-Réseau International des Instituts Pasteur (RIIP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Centre d'Investigation Clinique - Innovation Technologique de Lille - CIC 1403 - CIC 9301 (CIC Lille), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Evaluation des technologies de santé et des pratiques médicales - ULR 2694 (METRICS), Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Institut des Rencontres de la Forme [Wattignies] (IRFO), Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-Université de Lille, Université de Lille, Inserm, CHU Lille, Lille Inflammation Research International Center - U 995 [LIRIC], Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS], and Motricité, interactions, performance EA 4334 / Movement - Interactions - Performance [MIP]

Subjects
Male, Range of Motion, Percentile, Age Factors, Cardiorespiratory Fitness, Child, Exercise Test, Female, France, Healthy Volunteers, Humans, Muscle Strength, Range of Motion, Articular, Reference Values, Sex Factors, children, health, percentiles, physical fitness, Physical fitness, Physical Therapy, Sports Therapy and Rehabilitation, 030204 cardiovascular system & hematology, Overweight, 03 medical and health sciences, 0302 clinical medicine, medicine, Orthopedics and Sports Medicine, Analysis of covariance, [SDV.MHEP.PED]Life Sciences [q-bio]/Human health and pathology/Pediatrics, business.industry, Health related, Cardiorespiratory fitness, 030229 sport sciences, Reference values, Normative, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, medicine.symptom, business, Demography, Articular
Abstract

The primary objective of this study was to establish sex- and age-specific physical fitness percentiles for French children. The secondary aim was to assess sex, weight status, and age differences for physical fitness levels in French children. A sample of 31 484 children (16 023 boys, 15 461 girls) aged 6-11 years participated in the Diagnoform programme. Cardiorespiratory fitness, muscular endurance, speed, flexibility, and agility were assessed in this national programme. Percentile values were estimated as a function of age stratified by sex using a parametric method providing smooth centile curves and explicit formulae for the centile estimates. Values from the 10th to the 90th percentile are reported. The influence of body weight according to sex on the physical fitness level was also examined using an analysis of covariance adjusted for age. Physical fitness levels were slightly better in boys, except for agility and flexibility, in which girls performed better (Cohen's coefficient, 0.20-0.45; P

Published
2019
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5. Short-term air temperature forecasting using Nonparametric Functional Data Analysis and SARMA models

Author

Stelian Curceac, Taha B. M. J. Ouarda, Sophie Dabo Niang, Fateh Chebana, Camille Ternynck, MOdel for Data Analysis and Learning (MODAL), Laboratoire Paul Painlevé (LPP), Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Evaluation des technologies de santé et des pratiques médicales - ULR 2694 (METRICS), Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-École polytechnique universitaire de Lille (Polytech Lille), Lille économie management - UMR 9221 (LEM), Université d'Artois (UA)-Université catholique de Lille (UCL)-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Rothamsted Research, Biotechnology and Biological Sciences Research Council (BBSRC), Evaluation des technologies de santé et des pratiques médicales - ULR 2694 (METRICS), Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Masdar Institute of Science and Technology [Abu Dhabi], Institut National de la Recherche Scientifique [Québec] (INRS), Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Paul Painlevé - UMR 8524 (LPP), Centre National de la Recherche Scientifique (CNRS)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille-Evaluation des technologies de santé et des pratiques médicales - ULR 2694 (METRICS), and Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-École polytechnique universitaire de Lille (Polytech Lille)-Université de Lille, Sciences et Technologies

Subjects
Environmental Engineering, Time series, 010504 meteorology & atmospheric sciences, 0208 environmental biotechnology, 02 engineering and technology, 01 natural sciences, Air temperature, Bayesian information criterion, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Statistics, Autoregressive–moving-average model, Physics::Atmospheric and Oceanic Physics, 0105 earth and related environmental sciences, Mathematics, Parametric statistics, Ecological Modeling, Nonparametric statistics, Functional data analysis, Regression analysis, 020801 environmental engineering, SARMA, Nonlinear regression, Software, Smoothing, Forecasting
Abstract

International audience; Air temperature is a significant meteorological variable that affects social activities and economic sectors. In this paper, a non-parametric and a parametric approach are used to forecast hourly air temperature up to 24 h in advance. The former is a regression model in the Functional Data Analysis framework. The nonlinear regression operator is estimated using a kernel function. The smoothing parameter is obtained by a cross-validation procedure and used for the selection of the optimal number of closest curves. The other method applied is a Seasonal Autoregressive Moving Average (SARMA) model, the order of which is determined by the Bayesian Information Criterion. The obtained forecasts are combined using weights calculated based on the forecast errors. The results show that SARMA has a better performance for the first 6 forecasted hours, after which the Non-Parametric Functional Data Analysis (NPFDA) model provides superior results. Forecast pooling improves the accuracy of the forecasts.

Published
2019
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6. Streamflow hydrograph classification using functional data analysis

Author

Camille Ternynck, Taha B. M. J. Ouarda, Sophie Dabo-Niang, Fateh Chebana, Mohamed Ben Alaya, MOdel for Data Analysis and Learning (MODAL), Laboratoire Paul Painlevé (LPP), Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Evaluation des technologies de santé et des pratiques médicales - ULR 2694 (METRICS), Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-École polytechnique universitaire de Lille (Polytech Lille), Lille économie management - UMR 9221 (LEM), Université d'Artois (UA)-Université catholique de Lille (UCL)-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Economie Quantitative, Intégration, Politiques Publiques et Econométrie (EQUIPPE), Université de Lille, Sciences et Technologies-Université de Lille, Sciences Humaines et Sociales-PRES Université Lille Nord de France-Université de Lille, Droit et Santé, Institut National de la Recherche Scientifique [Québec] (INRS), Laboratoire Paul Painlevé - UMR 8524 (LPP), Centre National de la Recherche Scientifique (CNRS)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille-Université de Lille, Sciences et Technologies-Inria Lille - Nord Europe, and Université de Lille, Droit et Santé-PRES Université Lille Nord de France-Université de Lille, Sciences Humaines et Sociales-Université de Lille, Sciences et Technologies

Subjects
Atmospheric Science, Series (mathematics), Flood myth, 0208 environmental biotechnology, Data classification, Functional data analysis, Hydrograph, 02 engineering and technology, computer.software_genre, 020801 environmental engineering, Hydraulic structure, 13. Climate action, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Streamflow, Environmental science, Classification methods, Data mining, computer
Abstract

Classification of streamflow hydrographs plays an important role in a large number of hydrological and hydraulic studies. For instance, it allows decisions to be made regarding the implementation of hydraulic structures and characterization of different flood types, leading to a better understanding of extreme flow behavior. The employed hydrograph classification methods are generally based on a finite number of hydrograph characteristics and do not include all the available information contained in a discharge time series. In this paper, two statistical techniques from the theory of functional data classification are adapted and applied for the analysis of flood hydrographs. Functional classification directly employs all data of a discharge time series and thus contains all available information on shape, peak, and timing. This potentially allows a better understanding and treatment of floods as well as other hydrological phenomena. The considered functional methodology is applied to streamflow datasets from the province of Quebec, Canada. It is shown that classes obtained using functional approaches have merit and can lead to better representation than those obtained using a multidimensional hierarchical classification method. The considered methodology has the advantage of using all of the information contained in the hydrograph, thus reducing the subjectivity that is inherent in multidimensional analysis of the type and number of characteristics to be used and consequently diminishing the associated uncertainty.

Published
2015
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7. A new spatial regression estimator in the multivariate context

Author

Camille Ternynck, Anne-Françoise Yao, Sophie Dabo-Niang, MOdel for Data Analysis and Learning (MODAL), Laboratoire Paul Painlevé (LPP), Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Evaluation des technologies de santé et des pratiques médicales - ULR 2694 (METRICS), Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-École polytechnique universitaire de Lille (Polytech Lille), Equipe Probabilités, Analyse et Statistique, Laboratoire de Mathématiques Blaise Pascal (LMBP), Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-Centre National de la Recherche Scientifique (CNRS)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-Centre National de la Recherche Scientifique (CNRS), Economie Quantitative, Intégration, Politiques Publiques et Econométrie (EQUIPPE), Université de Lille, Sciences et Technologies-Université de Lille, Sciences Humaines et Sociales-PRES Université Lille Nord de France-Université de Lille, Droit et Santé, Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-Centre National de la Recherche Scientifique (CNRS), Université de Lille, Droit et Santé-PRES Université Lille Nord de France-Université de Lille, Sciences Humaines et Sociales-Université de Lille, Sciences et Technologies, Laboratoire Paul Painlevé - UMR 8524 (LPP), Centre National de la Recherche Scientifique (CNRS)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille-Université de Lille, Sciences et Technologies-Inria Lille - Nord Europe, Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Paul Painlevé - UMR 8524 (LPP), Centre National de la Recherche Scientifique (CNRS)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille-Evaluation des technologies de santé et des pratiques médicales - ULR 2694 (METRICS), Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-Université de Lille-École polytechnique universitaire de Lille (Polytech Lille)-Université de Lille, Sciences et Technologies, and Dabo-Niang, Sophie

Subjects
Discrete mathematics, Multivariate statistics, [STAT.AP]Statistics [stat]/Applications [stat.AP], [STAT.ME] Statistics [stat]/Methodology [stat.ME], [STAT.TH] Statistics [stat]/Statistics Theory [stat.TH], 010102 general mathematics, Nonparametric statistics, Estimator, General Medicine, [STAT.TH]Statistics [stat]/Statistics Theory [stat.TH], 01 natural sciences, 010104 statistics & probability, Minimum-variance unbiased estimator, [STAT.AP] Statistics [stat]/Applications [stat.AP], MathML, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Norm (mathematics), Statistics, Consistent estimator, 0101 mathematics, Minimax estimator, [STAT.ME]Statistics [stat]/Methodology [stat.ME], Mathematics
Abstract

International audience; In this note, we propose a nonparametric spatial estimator of the regression function View the MathML sourcex→r(x):=E[Yi|Xi=x],x∈Rd, of a stationary (d+1)(d+1)-dimensional spatial process View the MathML source{(Yi,Xi),i∈ZN}, at a point located at some station j. The proposed estimator depends on two kernels in order to control both the distance between observations and the spatial locations. Almost complete convergence and consistency in LqLq norm (q∈N⁎)(q∈N⁎) of the kernel estimate are obtained when the sample considered is an α-mixing sequence.

Published
2015
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