We present a novel formulation for discriminative anatomy detection in high dimensional neuroimaging data. While most studies solve this problem using mass univariate approaches, recent works show better accuracy and variable selection using a sparse classification model. Such methods typically use an l1 penalty for imposing sparseness and a graph net (GN) or a total variation (TV) penalty for ...

In this paper, the high-dimensional sparse linear regression model is considered, where the overall number of variables is larger than the number of observations. We investigate the L1 penalized least absolute deviation method. Different from most of other methods, the L1 penalized LAD method does not need any knowledge of standard deviation of the noises or any moment assumptions of the noises...

The logic LAE discussed in this paper is based on an approximate entailment relation. LAE generalises classical propositional logic to the effect that conclusions can be drawn with a quantified imprecision. To this end, properties are modelled by subsets of a distance space and statements are of the form that one property implies another property within a certain limit of tolerance. We adopt th...

Irrigation water management is crucial for agricultural production and livelihood security in Morocco as in many other parts of the world. For the implementation of an effective water management knowledge about farmers’ irrigation water demand is crucial to assess demand reactions of a water pricing policy, to establish a cost-benefit analysis of water supply investments or to determine the opt...

This paper presents an original filtering approach, called SND (Scoringbased Neighborhood Dominance), for the subgraph isomorphism problem. By reasoning on vertex dominance properties based on various scoring and neighborhood functions, SND appears to be a filtering mechanism of strong inference potential. For example, the recently proposed method LAD is a particular case of SND. We study a spe...

This paper derives asymptotic normality of a class of M-estimators in the generalized autoregressive conditional heteroskedastic ~GARCH! model+ The class of estimators includes least absolute deviation and Huber’s estimator in addition to the well-known quasi maximum likelihood estimator+ For some estimators, the asymptotic normality results are obtained only under the existence of fractional u...

Aim: Changes in the pH of chronic wounds can inhibit the optimal activity of various enzymes in the wound environment, thereby delaying wound healing. The aim of the present study is to monitor the effect of limited access dressing (LAD) on the pH on the surface of chronic wounds. Methods: A total of 140 patients with chronic wounds of more than 4 weeks duration were divided into two groups by ...

Abstract: The first-order moving average model or MA(1) is given by Xt = Zt − θ0Zt−1, with independent and identically distributed {Zt}. This is arguably the simplest time series model that one can write down. The MA(1) with unit root (θ0 = 1) arises naturally in a variety of time series applications. For example, if an underlying time series consists of a linear trend plus white noise errors, ...

The class of GARCH models has proved particularly valuable in modelling time series with time varying volatility. These include financial data, which can be particularly heavy tailed. It is well understood now that the tail heaviness of the innovation distribution plays an important role in determining the relative performance of the two competing estimation methods, namely the maximum quasilik...

Given a binary dataset of positive and negative observations, a positive (negative) pattern is a subcube having a nonempty intersection with the positive (negative) subset of the dataset, and an empty intersection with the negative (positive) subset of the dataset. Patterns are the key building blocks in Logical Analysis of Data (LAD), and are an essential tool in identifying the positive or ne...