PSR

Belief entropy, which represents the uncertainty measure between several pieces of evidence in the Dempster-Shafer framework, is attracting increasing interest in research. It has been used in many applications and is mainly based on the theory of evidence. To quantify uncertainty, several measures have been proposed in the literature. These measures, sometimes in extended or hybrid forms, use the Shannon entropy principle to determine uncertainty degree. However, the failure to consider the scale of the frame of discernment framework remains an open issue in quantifying uncertainty. In this p...

Belief entropy, which represents the uncertainty measure between several pieces of evidence in the Dempster-Shafer framework, is attracting increasing interest in research. It has been used in many applications and is mainly based on the theory of evidence. To quantify uncertainty, several measures have been proposed in the literature. These measures, sometimes in extended or hybrid forms, use the Shannon entropy principle to determine uncertainty degree. However, the failure to consider the scale of the frame of discernment framework remains an open issue in quantifying uncertainty. In this p...

In the medical field, it is essential to predict diseases early to prevent them. Diabetes is one of the most dangerous diseases all over the world. In modern lifestyles, sugar and fat are typically present in our dietary habits, which have increased the risk of diabetes. To predict the disease, it is extremely important to understand its symptoms. Currently, machine-learning (ML) algorithms are valuable for disease detection. This article presents a model using a fused machine learning approach for diabetes prediction. The conceptual framework consists of two types of models: Support Vector Ma...

In the medical field, it is essential to predict diseases early to prevent them. Diabetes is one of the most dangerous diseases all over the world. In modern lifestyles, sugar and fat are typically present in our dietary habits, which have increased the risk of diabetes. To predict the disease, it is extremely important to understand its symptoms. Currently, machine-learning (ML) algorithms are valuable for disease detection. This article presents a model using a fused machine learning approach for diabetes prediction. The conceptual framework consists of two types of models: Support Vector Ma...

In the medical field, it is essential to predict diseases early to prevent them. Diabetes is one of the most dangerous diseases all over the world. In modern lifestyles, sugar and fat are typically present in our dietary habits, which have increased the risk of diabetes. To predict the disease, it is extremely important to understand its symptoms. Currently, machine-learning (ML) algorithms are valuable for disease detection. This article presents a model using a fused machine learning approach for diabetes prediction. The conceptual framework consists of two types of models: Support Vector Ma...

In the medical field, it is essential to predict diseases early to prevent them. Diabetes is one of the most dangerous diseases all over the world. In modern lifestyles, sugar and fat are typically present in our dietary habits, which have increased the risk of diabetes. To predict the disease, it is extremely important to understand its symptoms. Currently, machine-learning (ML) algorithms are valuable for disease detection. This article presents a model using a fused machine learning approach for diabetes prediction. The conceptual framework consists of two types of models: Support Vector Ma...

In the medical field, it is essential to predict diseases early to prevent them. Diabetes is one of the most dangerous diseases all over the world. In modern lifestyles, sugar and fat are typically present in our dietary habits, which have increased the risk of diabetes. To predict the disease, it is extremely important to understand its symptoms. Currently, machine-learning (ML) algorithms are valuable for disease detection. This article presents a model using a fused machine learning approach for diabetes prediction. The conceptual framework consists of two types of models: Support Vector Ma...

In the medical field, it is essential to predict diseases early to prevent them. Diabetes is one of the most dangerous diseases all over the world. In modern lifestyles, sugar and fat are typically present in our dietary habits, which have increased the risk of diabetes. To predict the disease, it is extremely important to understand its symptoms. Currently, machine-learning (ML) algorithms are valuable for disease detection. This article presents a model using a fused machine learning approach for diabetes prediction. The conceptual framework consists of two types of models: Support Vector Ma...

In the medical field, it is essential to predict diseases early to prevent them. Diabetes is one of the most dangerous diseases all over the world. In modern lifestyles, sugar and fat are typically present in our dietary habits, which have increased the risk of diabetes. To predict the disease, it is extremely important to understand its symptoms. Currently, machine-learning (ML) algorithms are valuable for disease detection. This article presents a model using a fused machine learning approach for diabetes prediction. The conceptual framework consists of two types of models: Support Vector Ma...

In the medical field, it is essential to predict diseases early to prevent them. Diabetes is one of the most dangerous diseases all over the world. In modern lifestyles, sugar and fat are typically present in our dietary habits, which have increased the risk of diabetes. To predict the disease, it is extremely important to understand its symptoms. Currently, machine-learning (ML) algorithms are valuable for disease detection. This article presents a model using a fused machine learning approach for diabetes prediction. The conceptual framework consists of two types of models: Support Vector Ma...

AbstractIn today’s successful Learning Management System (LMS), gathering thousands of students, emergent collective dynamics drive innovative learning experiences where learners help each other in online forums. The benefits of those behaviors were theorized in Vygotsky’s socio-constructivism theory where he insists that the knowledge development of not so formal peer exchanges is beneficial to all participants. Observing and understanding how those dynamics occur could improve course design and help tutors intervene to sustain collective learning. But, although the scientific community ackno...

AbstractIn today’s successful Learning Management System (LMS), gathering thousands of students, emergent collective dynamics drive innovative learning experiences where learners help each other in online forums. The benefits of those behaviors were theorized in Vygotsky’s socio-constructivism theory where he insists that the knowledge development of not so formal peer exchanges is beneficial to all participants. Observing and understanding how those dynamics occur could improve course design and help tutors intervene to sustain collective learning. But, although the scientific community ackno...

AbstractIn today’s successful Learning Management System (LMS), gathering thousands of students, emergent collective dynamics drive innovative learning experiences where learners help each other in online forums. The benefits of those behaviors were theorized in Vygotsky’s socio-constructivism theory where he insists that the knowledge development of not so formal peer exchanges is beneficial to all participants. Observing and understanding how those dynamics occur could improve course design and help tutors intervene to sustain collective learning. But, although the scientific community ackno...

AbstractIn today’s successful Learning Management System (LMS), gathering thousands of students, emergent collective dynamics drive innovative learning experiences where learners help each other in online forums. The benefits of those behaviors were theorized in Vygotsky’s socio-constructivism theory where he insists that the knowledge development of not so formal peer exchanges is beneficial to all participants. Observing and understanding how those dynamics occur could improve course design and help tutors intervene to sustain collective learning. But, although the scientific community ackno...

Abstract The promise of an easy access to a virtually unlimited number of resources makes Infrastructure as a Service Clouds a good candidate for the execution of data‐intensive workflow applications composed of hundreds of computational tasks. Thanks to a careful execution planning, workflow management systems can build a tailored compute infrastructure by combining a set of virtual machine instances. However, these applications usually rely on files to handle dependencies between tasks. A storage space shared by all virtual machines may become a bottleneck and badly impact the application ex...

Abstract The promise of an easy access to a virtually unlimited number of resources makes Infrastructure as a Service Clouds a good candidate for the execution of data‐intensive workflow applications composed of hundreds of computational tasks. Thanks to a careful execution planning, workflow management systems can build a tailored compute infrastructure by combining a set of virtual machine instances. However, these applications usually rely on files to handle dependencies between tasks. A storage space shared by all virtual machines may become a bottleneck and badly impact the application ex...

Abstract The promise of an easy access to a virtually unlimited number of resources makes Infrastructure as a Service Clouds a good candidate for the execution of data‐intensive workflow applications composed of hundreds of computational tasks. Thanks to a careful execution planning, workflow management systems can build a tailored compute infrastructure by combining a set of virtual machine instances. However, these applications usually rely on files to handle dependencies between tasks. A storage space shared by all virtual machines may become a bottleneck and badly impact the application ex...

Abstract The promise of an easy access to a virtually unlimited number of resources makes Infrastructure as a Service Clouds a good candidate for the execution of data‐intensive workflow applications composed of hundreds of computational tasks. Thanks to a careful execution planning, workflow management systems can build a tailored compute infrastructure by combining a set of virtual machine instances. However, these applications usually rely on files to handle dependencies between tasks. A storage space shared by all virtual machines may become a bottleneck and badly impact the application ex...

Abstract The promise of an easy access to a virtually unlimited number of resources makes Infrastructure as a Service Clouds a good candidate for the execution of data‐intensive workflow applications composed of hundreds of computational tasks. Thanks to a careful execution planning, workflow management systems can build a tailored compute infrastructure by combining a set of virtual machine instances. However, these applications usually rely on files to handle dependencies between tasks. A storage space shared by all virtual machines may become a bottleneck and badly impact the application ex...

Structural development defects essentially refer to code structure that violates object-oriented design principles. They make program maintenance challenging and deteriorate software quality over time. Various detection approaches, ranging from traditional heuristic algorithms to machine learning methods, are used to identify these defects. Ensemble learning methods have strengthened the detection of these defects. However, existing approaches do not simultaneously exploit the capabilities of extracting relevant features from pre-trained models and the performance of neural networks for the cl...

Structural development defects essentially refer to code structure that violates object-oriented design principles. They make program maintenance challenging and deteriorate software quality over time. Various detection approaches, ranging from traditional heuristic algorithms to machine learning methods, are used to identify these defects. Ensemble learning methods have strengthened the detection of these defects. However, existing approaches do not simultaneously exploit the capabilities of extracting relevant features from pre-trained models and the performance of neural networks for the cl...

Structural development defects essentially refer to code structure that violates object-oriented design principles. They make program maintenance challenging and deteriorate software quality over time. Various detection approaches, ranging from traditional heuristic algorithms to machine learning methods, are used to identify these defects. Ensemble learning methods have strengthened the detection of these defects. However, existing approaches do not simultaneously exploit the capabilities of extracting relevant features from pre-trained models and the performance of neural networks for the cl...

Structural development defects essentially refer to code structure that violates object-oriented design principles. They make program maintenance challenging and deteriorate software quality over time. Various detection approaches, ranging from traditional heuristic algorithms to machine learning methods, are used to identify these defects. Ensemble learning methods have strengthened the detection of these defects. However, existing approaches do not simultaneously exploit the capabilities of extracting relevant features from pre-trained models and the performance of neural networks for the cl...

Structural development defects essentially refer to code structure that violates object-oriented design principles. They make program maintenance challenging and deteriorate software quality over time. Various detection approaches, ranging from traditional heuristic algorithms to machine learning methods, are used to identify these defects. Ensemble learning methods have strengthened the detection of these defects. However, existing approaches do not simultaneously exploit the capabilities of extracting relevant features from pre-trained models and the performance of neural networks for the cl...

ABSTRACT Multispectral and multimodal unstained blood smear images are obtained and evaluated to offer computer‐assisted automated diagnostic evidence for malaria. However, these images suffer from uneven lighting, contrast variability, and local luminosity due to the acquisition system. This limitation significantly impacts the diagnostic process and its overall outcomes. To overcome this limitation, it is crucial to perform normalization on the acquired multispectral images as a preprocessing step for malaria parasite detection. In this study, we propose a novel method for achieving this nor...

ABSTRACT Multispectral and multimodal unstained blood smear images are obtained and evaluated to offer computer‐assisted automated diagnostic evidence for malaria. However, these images suffer from uneven lighting, contrast variability, and local luminosity due to the acquisition system. This limitation significantly impacts the diagnostic process and its overall outcomes. To overcome this limitation, it is crucial to perform normalization on the acquired multispectral images as a preprocessing step for malaria parasite detection. In this study, we propose a novel method for achieving this nor...

ABSTRACT Multispectral and multimodal unstained blood smear images are obtained and evaluated to offer computer‐assisted automated diagnostic evidence for malaria. However, these images suffer from uneven lighting, contrast variability, and local luminosity due to the acquisition system. This limitation significantly impacts the diagnostic process and its overall outcomes. To overcome this limitation, it is crucial to perform normalization on the acquired multispectral images as a preprocessing step for malaria parasite detection. In this study, we propose a novel method for achieving this nor...

ABSTRACT Multispectral and multimodal unstained blood smear images are obtained and evaluated to offer computer‐assisted automated diagnostic evidence for malaria. However, these images suffer from uneven lighting, contrast variability, and local luminosity due to the acquisition system. This limitation significantly impacts the diagnostic process and its overall outcomes. To overcome this limitation, it is crucial to perform normalization on the acquired multispectral images as a preprocessing step for malaria parasite detection. In this study, we propose a novel method for achieving this nor...

AbstractZero-inflated bivariate Poisson regression (ZIBP) models are most often applied to correlated bivariate count data that contain a large proportion of zeros. These models have been applied in various fields, such as medical research, health economics, insurance, sports, etc. Because, in practice, some of the covariates involved in ZIBP regression modeling often have missing values, we propose methods for estimating the parameters of the ZIBP regression model with missing at random covariates. Assuming that the selection probability is unknown and estimated non parametrically (by a kerne...

AbstractZero-inflated bivariate Poisson regression (ZIBP) models are most often applied to correlated bivariate count data that contain a large proportion of zeros. These models have been applied in various fields, such as medical research, health economics, insurance, sports, etc. Because, in practice, some of the covariates involved in ZIBP regression modeling often have missing values, we propose methods for estimating the parameters of the ZIBP regression model with missing at random covariates. Assuming that the selection probability is unknown and estimated non parametrically (by a kerne...

AbstractZero-inflated bivariate Poisson regression (ZIBP) models are most often applied to correlated bivariate count data that contain a large proportion of zeros. These models have been applied in various fields, such as medical research, health economics, insurance, sports, etc. Because, in practice, some of the covariates involved in ZIBP regression modeling often have missing values, we propose methods for estimating the parameters of the ZIBP regression model with missing at random covariates. Assuming that the selection probability is unknown and estimated non parametrically (by a kerne...

These last decades, numerous signal enhancement techniques to push away the detection limits were born. Most of them implement the use of supports with specific properties. Indeed, supports made of thin layers, or micro- or nano-structured layers are present in topics such as unlabelling biological analysis, RAMAN spectroscopy, SNOM microscopy... Contrast enhanced supports were also developed for the optical microscopy but their performances remained so far very limited. Indeed, the optical microscopy has particular constraints due to the geometry of the incidental lightbeam. Recently, a study...

These last decades, numerous signal enhancement techniques to push away the detection limits were born. Most of them implement the use of supports with specific properties. Indeed, supports made of thin layers, or micro- or nano-structured layers are present in topics such as unlabelling biological analysis, RAMAN spectroscopy, SNOM microscopy... Contrast enhanced supports were also developed for the optical microscopy but their performances remained so far very limited. Indeed, the optical microscopy has particular constraints due to the geometry of the incidental lightbeam. Recently, a study...

Résumé (VO) Abstract On considère le processus linéaire (Xt,t∈Z) à valeurs dans R, défini de la manière suivante : Xt=∑i=0∞ai(θ)εt−i où (εt)t∈Z est une suite de variables aléatoires dans R, indépendantes et identiquement distribuées, et θ∈Θ avec Θ⊂Rq. Xt est supposé être un processus gaussien à longue mémoire. On se propose, dans cette note, d'estimer le paramètre θ par la méthode du minimum de distance de Hellinger. On établit, sous certaines conditions, des théorèmes limites de l'estimateur ainsi obtenu. We consider the real-valued linear process (Xt,t∈Z) which is defined as: Xt=∑i=0∞ai(θ)εt...

Résumé (VO) Abstract On considère le processus linéaire (Xt,t∈Z) à valeurs dans R, défini de la manière suivante : Xt=∑i=0∞ai(θ)εt−i où (εt)t∈Z est une suite de variables aléatoires dans R, indépendantes et identiquement distribuées, et θ∈Θ avec Θ⊂Rq. Xt est supposé être un processus gaussien à longue mémoire. On se propose, dans cette note, d'estimer le paramètre θ par la méthode du minimum de distance de Hellinger. On établit, sous certaines conditions, des théorèmes limites de l'estimateur ainsi obtenu. We consider the real-valued linear process (Xt,t∈Z) which is defined as: Xt=∑i=0∞ai(θ)εt...

Résumé (VO) Abstract On considère le processus linéaire (Xt,t∈Z) à valeurs dans R, défini de la manière suivante : Xt=∑i=0∞ai(θ)εt−i où (εt)t∈Z est une suite de variables aléatoires dans R, indépendantes et identiquement distribuées, et θ∈Θ avec Θ⊂Rq. Xt est supposé être un processus gaussien à longue mémoire. On se propose, dans cette note, d'estimer le paramètre θ par la méthode du minimum de distance de Hellinger. On établit, sous certaines conditions, des théorèmes limites de l'estimateur ainsi obtenu. We consider the real-valued linear process (Xt,t∈Z) which is defined as: Xt=∑i=0∞ai(θ)εt...

Résumé (VO) Abstract On considère le processus linéaire (Xt,t∈Z) à valeurs dans R, défini de la manière suivante : Xt=∑i=0∞ai(θ)εt−i où (εt)t∈Z est une suite de variables aléatoires dans R, indépendantes et identiquement distribuées, et θ∈Θ avec Θ⊂Rq. Xt est supposé être un processus gaussien à longue mémoire. On se propose, dans cette note, d'estimer le paramètre θ par la méthode du minimum de distance de Hellinger. On établit, sous certaines conditions, des théorèmes limites de l'estimateur ainsi obtenu. We consider the real-valued linear process (Xt,t∈Z) which is defined as: Xt=∑i=0∞ai(θ)εt...

AbstractWe propose a new test to detect a change in the conditional mean of Nonparametric AutoRegressive processes. This test is based on the combining CUSUM and marked empirical processes. Our CUSUM test requires an estimator of the regression function to make it asymptotically distribution free under the no change null hypothesis. As a consequence, we obtain convergence of the developed test statistic. We show the asymptotic consistency of the test when the difference between the regression functions before and after the break is constant. We illustrate the applicability of our method by the...

AbstractWe propose a new test to detect a change in the conditional mean of Nonparametric AutoRegressive processes. This test is based on the combining CUSUM and marked empirical processes. Our CUSUM test requires an estimator of the regression function to make it asymptotically distribution free under the no change null hypothesis. As a consequence, we obtain convergence of the developed test statistic. We show the asymptotic consistency of the test when the difference between the regression functions before and after the break is constant. We illustrate the applicability of our method by the...

AbstractWe propose a new test to detect a change in the conditional mean of Nonparametric AutoRegressive processes. This test is based on the combining CUSUM and marked empirical processes. Our CUSUM test requires an estimator of the regression function to make it asymptotically distribution free under the no change null hypothesis. As a consequence, we obtain convergence of the developed test statistic. We show the asymptotic consistency of the test when the difference between the regression functions before and after the break is constant. We illustrate the applicability of our method by the...

The paper deals with the estimation of parameters of multidimensional diffusion processes that are discretely observed. We construct estimator of the parameters based on the minimum Hellinger distance method. This method is based on the minimization of the Hellinger distance between the density of the invariant distribution of the diffusion process and a nonparametric estimator of this density. We give conditions which ensure the existence of an invariant measure that admits density with respect to the Lebesgue measure and the strong mixing property with exponential rate for the Markov process...

The paper deals with the estimation of parameters of multidimensional diffusion processes that are discretely observed. We construct estimator of the parameters based on the minimum Hellinger distance method. This method is based on the minimization of the Hellinger distance between the density of the invariant distribution of the diffusion process and a nonparametric estimator of this density. We give conditions which ensure the existence of an invariant measure that admits density with respect to the Lebesgue measure and the strong mixing property with exponential rate for the Markov process...

The paper deals with the estimation of parameters of multidimensional diffusion processes that are discretely observed. We construct estimator of the parameters based on the minimum Hellinger distance method. This method is based on the minimization of the Hellinger distance between the density of the invariant distribution of the diffusion process and a nonparametric estimator of this density. We give conditions which ensure the existence of an invariant measure that admits density with respect to the Lebesgue measure and the strong mixing property with exponential rate for the Markov process...

The paper deals with the estimation of parameters of multidimensional diffusion processes that are discretely observed. We construct estimator of the parameters based on the minimum Hellinger distance method. This method is based on the minimization of the Hellinger distance between the density of the invariant distribution of the diffusion process and a nonparametric estimator of this density. We give conditions which ensure the existence of an invariant measure that admits density with respect to the Lebesgue measure and the strong mixing property with exponential rate for the Markov process...

We investigate the asymptotic behavior of the Nadaraya-Watson (NW) estimator of the regression function of a τ−mixing process. We prove the strong consistency and the asymptotic normality of this estimator and we illustrate these two properties using simulated data.

We investigate the asymptotic behavior of the Nadaraya-Watson (NW) estimator of the regression function of a τ−mixing process. We prove the strong consistency and the asymptotic normality of this estimator and we illustrate these two properties using simulated data.

We investigate the asymptotic behavior of the Nadaraya-Watson (NW) estimator of the regression function of a τ−mixing process. We prove the strong consistency and the asymptotic normality of this estimator and we illustrate these two properties using simulated data.

(2003). Hierarchical Linear Models: Applications and Data Analysis Methods. Journal of the American Statistical Association: Vol. 98, No. 463, pp. 767-768.

We estimate the parameters of a nonstationary multivariate ARFIMA (AutoRegressive Fractionally Integrated Moving Average) process by the quasi likelihood approach. Then, we define the pseudo-spectral density of the process. Under some assumptions, we establish consistency, asymptotic normality.

We estimate the parameters of a nonstationary multivariate ARFIMA (AutoRegressive Fractionally Integrated Moving Average) process by the quasi likelihood approach. Then, we define the pseudo-spectral density of the process. Under some assumptions, we establish consistency, asymptotic normality.

We estimate the parameters of a nonstationary multivariate ARFIMA (AutoRegressive Fractionally Integrated Moving Average) process by the quasi likelihood approach. Then, we define the pseudo-spectral density of the process. Under some assumptions, we establish consistency, asymptotic normality.

We estimate the parameters of a nonstationary multivariate ARFIMA (AutoRegressive Fractionally Integrated Moving Average) process by the quasi likelihood approach. Then, we define the pseudo-spectral density of the process. Under some assumptions, we establish consistency, asymptotic normality.

Abstract (VO) Résumé We address the problem of parameter estimation of long memory time series. We consider k-factors Gegenbauer Autoregressive Moving Average (k-GARMA) processes and we estimate their parameters by the minimum Hellinger distance estimator. We establish the consistency of the estimator and the asymptotic normality for some bandwidth choice. Nous étudions le problème dʼestimation dans les séries temporelles fortement dépendantes. Nous considérons les processus Gegenbaeur autorégressifs à moyenne mobile (GARMA) à k facteurs pour les modéliser et nous estimons leurs paramètres par...

Abstract (VO) Résumé We address the problem of parameter estimation of long memory time series. We consider k-factors Gegenbauer Autoregressive Moving Average (k-GARMA) processes and we estimate their parameters by the minimum Hellinger distance estimator. We establish the consistency of the estimator and the asymptotic normality for some bandwidth choice. Nous étudions le problème dʼestimation dans les séries temporelles fortement dépendantes. Nous considérons les processus Gegenbaeur autorégressifs à moyenne mobile (GARMA) à k facteurs pour les modéliser et nous estimons leurs paramètres par...

AbstractZero-inflated models are statistical tools that can be used to assess the relationships between covariates and a count result that contains an excessive number of zero-inflated counts. However, these models do not allow analysis of the effects of the covariates on the overall population of the mixture, namely on the marginal mean of the zero-inflated count. For this purpose, marginal zero-inflated models, such as marginal zero-inflated Poisson models, have been developed. Most often, these models are restricted to data whose overdispersion is due solely to the high proportion of zeros...

AbstractZero-inflated models are statistical tools that can be used to assess the relationships between covariates and a count result that contains an excessive number of zero-inflated counts. However, these models do not allow analysis of the effects of the covariates on the overall population of the mixture, namely on the marginal mean of the zero-inflated count. For this purpose, marginal zero-inflated models, such as marginal zero-inflated Poisson models, have been developed. Most often, these models are restricted to data whose overdispersion is due solely to the high proportion of zeros...

AbstractZero-inflated models are statistical tools that can be used to assess the relationships between covariates and a count result that contains an excessive number of zero-inflated counts. However, these models do not allow analysis of the effects of the covariates on the overall population of the mixture, namely on the marginal mean of the zero-inflated count. For this purpose, marginal zero-inflated models, such as marginal zero-inflated Poisson models, have been developed. Most often, these models are restricted to data whose overdispersion is due solely to the high proportion of zeros...

The lack of an operational definition for hunger has been frequently cited as a barrier to progress in addressing the problem. The purposes of this research were to develop an understanding of hunger from the perspective of women who had experienced it and to construct and evaluate indicators to measure hunger directly in similar populations. In-depth interviews were conducted with 32 women of childbearing age from rural and urban areas of Upstate New York. Qualitative analysis of the responses yielded a conceptualization of hunger that included two levels: the individual and household. Hunger...

The lack of an operational definition for hunger has been frequently cited as a barrier to progress in addressing the problem. The purposes of this research were to develop an understanding of hunger from the perspective of women who had experienced it and to construct and evaluate indicators to measure hunger directly in similar populations. In-depth interviews were conducted with 32 women of childbearing age from rural and urban areas of Upstate New York. Qualitative analysis of the responses yielded a conceptualization of hunger that included two levels: the individual and household. Hunger...

The lack of an operational definition for hunger has been frequently cited as a barrier to progress in addressing the problem. The purposes of this research were to develop an understanding of hunger from the perspective of women who had experienced it and to construct and evaluate indicators to measure hunger directly in similar populations. In-depth interviews were conducted with 32 women of childbearing age from rural and urban areas of Upstate New York. Qualitative analysis of the responses yielded a conceptualization of hunger that included two levels: the individual and household. Hunger...

The lack of an operational definition for hunger has been frequently cited as a barrier to progress in addressing the problem. The purposes of this research were to develop an understanding of hunger from the perspective of women who had experienced it and to construct and evaluate indicators to measure hunger directly in similar populations. In-depth interviews were conducted with 32 women of childbearing age from rural and urban areas of Upstate New York. Qualitative analysis of the responses yielded a conceptualization of hunger that included two levels: the individual and household. Hunger...

The lack of an operational definition for hunger has been frequently cited as a barrier to progress in addressing the problem. The purposes of this research were to develop an understanding of hunger from the perspective of women who had experienced it and to construct and evaluate indicators to measure hunger directly in similar populations. In-depth interviews were conducted with 32 women of childbearing age from rural and urban areas of Upstate New York. Qualitative analysis of the responses yielded a conceptualization of hunger that included two levels: the individual and household. Hunger...

We introduce a nonparametric estimation of a multiple order conditional within-subject correlation of a continuous times stochastic process X={X(t),t∈[0,T]} defined on a probability space (Ω,A,P). We prove the asymptotic normality of the conditional within-subject covariance estimators. Nous introduisons une estimation non paramétrique de la corrélation intra-objet dʼordre multiple dʼun processus stochastique X={X(t),t∈[0,T]} défini sur un espace de probabilité (Ω,A,P). Nous établissons la normalité asymptotique des estimateurs de la covariance conditionnelle intra-objet.

We introduce a nonparametric estimation of a multiple order conditional within-subject correlation of a continuous times stochastic process X={X(t),t∈[0,T]} defined on a probability space (Ω,A,P). We prove the asymptotic normality of the conditional within-subject covariance estimators. Nous introduisons une estimation non paramétrique de la corrélation intra-objet dʼordre multiple dʼun processus stochastique X={X(t),t∈[0,T]} défini sur un espace de probabilité (Ω,A,P). Nous établissons la normalité asymptotique des estimateurs de la covariance conditionnelle intra-objet.

We introduce a nonparametric estimation of a multiple order conditional within-subject correlation of a continuous times stochastic process X={X(t),t∈[0,T]} defined on a probability space (Ω,A,P). We prove the asymptotic normality of the conditional within-subject covariance estimators. Nous introduisons une estimation non paramétrique de la corrélation intra-objet dʼordre multiple dʼun processus stochastique X={X(t),t∈[0,T]} défini sur un espace de probabilité (Ω,A,P). Nous établissons la normalité asymptotique des estimateurs de la covariance conditionnelle intra-objet.

We introduce a nonparametric estimation of a multiple order conditional within-subject correlation of a continuous times stochastic process X={X(t),t∈[0,T]} defined on a probability space (Ω,A,P). We prove the asymptotic normality of the conditional within-subject covariance estimators. Nous introduisons une estimation non paramétrique de la corrélation intra-objet dʼordre multiple dʼun processus stochastique X={X(t),t∈[0,T]} défini sur un espace de probabilité (Ω,A,P). Nous établissons la normalité asymptotique des estimateurs de la covariance conditionnelle intra-objet.

In this note, we consider an m-dimensional stationary multivariate long memory ARFIMA (AutoRegressive Fractionally Integrated Moving Average) process, which is defined as :, where M denotes the transpose of the matrix M .We determine the minimum Hellinger distance estimator (MHDE) of the parameters of a stationary multivariate long memory ARFIMA.This method is based on the minimization of the Hellinger distance between the random function of f n (.) and a theoretical probability density f θ (.).We establish, under some assumptions, the almost sure convergence of the estimator and its asymptoti...

In this note, we consider an m-dimensional stationary multivariate long memory ARFIMA (AutoRegressive Fractionally Integrated Moving Average) process, which is defined as :, where M denotes the transpose of the matrix M .We determine the minimum Hellinger distance estimator (MHDE) of the parameters of a stationary multivariate long memory ARFIMA.This method is based on the minimization of the Hellinger distance between the random function of f n (.) and a theoretical probability density f θ (.).We establish, under some assumptions, the almost sure convergence of the estimator and its asymptoti...

This paper investigates change point detection within financial time series volatility using the NonParametric AutoRegressive Conditionally Heteroscedastic (NPARCH) process. It introduces a classical CUSUM type change point test following the test procedure by [19]. In the absence of change (Null Hypothesis), the test statistic converges to a well-established distribution, simplifying the determination of the critical test value. Conversely, under the Alternative Hypothesis, the probability that the test statistic tends to infinity is one, indicating asymptotic power one. Simulation results ar...

This paper investigates change point detection within financial time series volatility using the NonParametric AutoRegressive Conditionally Heteroscedastic (NPARCH) process. It introduces a classical CUSUM type change point test following the test procedure by [19]. In the absence of change (Null Hypothesis), the test statistic converges to a well-established distribution, simplifying the determination of the critical test value. Conversely, under the Alternative Hypothesis, the probability that the test statistic tends to infinity is one, indicating asymptotic power one. Simulation results ar...

This paper investigates change point detection within financial time series volatility using the NonParametric AutoRegressive Conditionally Heteroscedastic (NPARCH) process. It introduces a classical CUSUM type change point test following the test procedure by [19]. In the absence of change (Null Hypothesis), the test statistic converges to a well-established distribution, simplifying the determination of the critical test value. Conversely, under the Alternative Hypothesis, the probability that the test statistic tends to infinity is one, indicating asymptotic power one. Simulation results ar...

This paper discusses the robustness of suitability tests using the empirical copula process. It compares the empirical copula with a parametric estimate of the copula obtained under the null hypothesis. Fitting tests using Kendall inversion and maximum likelihood estimation methods may be less effective, especially at the sight of outliers. We recommend to establish a goodness-of-fit procedure with the Maximum Mean Discrepancy (MMD) estimator, a robust estimator, in order to assess the empirical size and power of the test under several families of alternative copulas. Then a comparative large-...

This paper discusses the robustness of suitability tests using the empirical copula process. It compares the empirical copula with a parametric estimate of the copula obtained under the null hypothesis. Fitting tests using Kendall inversion and maximum likelihood estimation methods may be less effective, especially at the sight of outliers. We recommend to establish a goodness-of-fit procedure with the Maximum Mean Discrepancy (MMD) estimator, a robust estimator, in order to assess the empirical size and power of the test under several families of alternative copulas. Then a comparative large-...

The purpose of this work is to nonparametrically estimate the conditional quantile for a locally stationary multivariate spatial process. The new kernel quantile estimate derived from the one of conditional distribution function (CDF). The originality in the paper is based on the ability to take into account some local spatial dependency in estimate CDF form. Consistency and asymptotic normality of the estimates are obtained under ‐mixing condition. Numerical study and application to real data are given in order to illustrate the performance of our methodology.

The purpose of this work is to nonparametrically estimate the conditional quantile for a locally stationary multivariate spatial process. The new kernel quantile estimate derived from the one of conditional distribution function (CDF). The originality in the paper is based on the ability to take into account some local spatial dependency in estimate CDF form. Consistency and asymptotic normality of the estimates are obtained under ‐mixing condition. Numerical study and application to real data are given in order to illustrate the performance of our methodology.

The purpose of this work is to nonparametrically estimate the conditional quantile for a locally stationary multivariate spatial process. The new kernel quantile estimate derived from the one of conditional distribution function (CDF). The originality in the paper is based on the ability to take into account some local spatial dependency in estimate CDF form. Consistency and asymptotic normality of the estimates are obtained under ‐mixing condition. Numerical study and application to real data are given in order to illustrate the performance of our methodology.

The purpose of this work is to nonparametrically estimate the conditional quantile for a locally stationary multivariate spatial process. The new kernel quantile estimate derived from the one of conditional distribution function (CDF). The originality in the paper is based on the ability to take into account some local spatial dependency in estimate CDF form. Consistency and asymptotic normality of the estimates are obtained under ‐mixing condition. Numerical study and application to real data are given in order to illustrate the performance of our methodology.