Smoothed L-estimation of Regression Function

The Nadaraya-Watson nonparametric estimator of regression is known to be highly sensitive to the presence of outliers in data.This sensitivity can be reduced, for example, by using local L-estimates of regression.Whereas the local L-estimation is traditionally done using an empirical conditional distribution function, we propose to use instead a smoothed conditional distribution function.The asymptotic distribution of the proposed estimator is derived under mild ¯-mixing conditions, and additionally, we show that the smoothed L-estimation approach provides computational as well as statistical ¯nite-sample improvements.Finally, the proposed method is applied to the modelling of implied volatilitynonparametric regression;L-estimation;smoothed cumulative distribution function

Smoothed L-estimation of Regression Function

The Nadaraya-Watson nonparametric estimator of regression is known to be highly sensitive to the presence of outliers in data.This sensitivity can be reduced, for example, by using local L-estimates of regression.Whereas the local L-estimation is traditionally done using an empirical conditional distribution function, we propose to use instead a smoothed conditional distribution function.The asymptotic distribution of the proposed estimator is derived under mild ¯-mixing conditions, and additionally, we show that the smoothed L-estimation approach provides computational as well as statistical ¯nite-sample improvements.Finally, the proposed method is applied to the modelling of implied volatilit

Tongue pressure production against hard palate during supraglottic swallow and super supraglottic swallow

Introduction: Supraglottic swallow (SS) and super supraglottic swallow (SSS) are voluntary airway protection techniques which are widely used in the rehabilitation of patients with dysphagia. Although there have been some reports about the effect of these techniques on the pharyngeal stage of swallowing, little is known about that on the oral stage. The purpose of this study is to investigate the influence of SS and SSS on the state of tongue-palate contact by measuring tongue pressure production against hard palate. Materials and Methods: Nineteen healthy adults (six males and thirteen females, average age; 25.9 years) participated in this study. Tongue pressure during swallowing 5 ml water with normal swallow, SS and SSS was measured by using an ultra-thin tongue pressure sensor sheet with 5 pressure-sensing points attached to the hard palate. Maximal magnitude, duration and integrated value of tongue pressure were analyzed based on the tongue pressure waveform obtained. Results and Discussion: Maximal magnitude and integrated values during SS were higher at posterior part of the hard palate than those during normal swallow, and those during SSS were higher at each part of the hard palate than those during normal swallow. In addition, those during SSS were higher at anterior-median part and posterior circumferential part of the hard palate than those during SS. These results suggest that SS and SSS facilitated tongue-palate contact which might improve bolus driving force in the oral stage. SSS was more effective than SS in this respect

CAS-MINE: Providing personalized services in context-aware applications by means of generalized rules

Context-aware systems acquire and exploit information on the user context to tailor services to a particular user, place, time, and/or event. Hence, they allowservice providers to adapt their services to actual user needs, by offering personalized services depending on the current user context. Service providers are usually interested in profiling users both to increase client satisfaction and to broaden the set of offered services. Novel and efficient techniques are needed to tailor service supply to the user (or the user category) and to the situation inwhich he/she is involved. This paper presents the CAS-Mine framework to efficiently discover relevant relationships between user context data and currently asked services for both user and service profiling. CAS-Mine efficiently extracts generalized association rules, which provide a high-level abstraction of both user habits and service characteristics depending on the context. A lazy (analyst-provided) taxonomy evaluation performed on different attributes (e.g., a geographic hierarchy on spatial coordinates, a classification of provided services) drives the rule generalization process. Extracted rules are classified into groups according to their semantic meaning and ranked by means of quality indices, thus allowing a domain expert to focus on the most relevant patterns. Experiments performed on three context-aware datasets, obtained by logging user requests and context information for three real applications, show the effectiveness and the efficiency of the CAS-Mine framework in mining different valuable types of correlations between user habits, context information, and provided services

Categorization of the context within the medical domain

The context itself has multiple meanings may vary according to the domain of application. This contextual flexibility was behind the emergence of so such huge number of context definitions. Nevertheless, all the proposed definitions do not provide solid ground for systems developers’ expectations, especially in healthcare domain [1]. This issue prompted researchers to divide the context into a set of concepts that would facilitate organizing of contextual knowledge. The conventional taxonomies of context are always too complex, and we need to fight to make them useful in the intended application area. In this paper, we propose a new context classification which covers almost all the context aspects that we may need to develop a tele-monitoring system for chronic disease management

Smoothed L-estimation of regression function

The Nadaraya–Watson nonparametric estimator of regression is known to be highly sensitive to the presence of outliers in data. This sensitivity can be reduced, for example, by using local L-estimates of regression. Whereas the local L-estimation is traditionally done using an empirical conditional distribution function, we propose to use instead a smoothed conditional distribution function. The asymptotic distribution of the proposed estimator is derived under mild β-mixing conditions, and additionally, we show that the smoothed L-estimation approach provides computational as well as statistical finite-sample improvements. Finally, the proposed method is applied to the modelling of implied volatility

Smoothed L-estimation of regression function

The Nadaraya-Watson nonparametric estimator of regression is known to be highly sensitive to the presence of outliers in data. This sensitivity can be reduced, for example, by using local L-estimates of regression. Whereas the local L-estimation is traditionally done using an empirical conditional distribution function, we propose to use instead a smoothed conditional distribution function. The asymptotic distribution of the proposed estimator is derived under mild [beta]-mixing conditions, and additionally, we show that the smoothed L-estimation approach provides computational as well as statistical finite-sample improvements. Finally, the proposed method is applied to the modelling of implied volatility.