Nonparametric forecasting: a comparison of three kernel-based methods

In this paper the use of three kernel-based nonparametric forecasting methods - the conditional mean, the conditional median, and the conditioal mode - is explored in detail. Several issues related to the estimation of these methods are discussed, including the choice of the bandwidth and the type of kernel function. The out-of-sample forecasting performance of the three nonparametric methods is investigated using 60 real time series. We find that there is no superior forecast method for series having approximately less than 100 observations. However, when a time series is long or when its conditional density is bimodal there is quite a differenc between the forecasting performance f the three kernel-based forecasting methods

Thoughts on entering correlated imaging variables into a multivariable model: Application to radiomics and texture analysis

International audienc

Interobserver agreement issues in radiology

International audienceAgreement between observers (i.e., inter-rater agreement) can be quantified with various criteria but their appropriate selections are critical. When the measure is qualitative (nominal or ordinal), the proportion of agreement or the kappa coefficient should be used to evaluate inter-rater consistency (i.e., inter-rater reliability). The kappa coefficient is more meaningful that the raw percentage of agreement, because the latter does not account for agreements due to chance alone. When the measures are quantitative, the intraclass correlation coefficient (ICC) should be used to assess agreement but this should be done with care because there are different ICCs so that it is important to describe the model and type of ICC being used. The Bland-Altman method can be used to assess consistency and conformity but its use should be restricted to comparison of two raters. (C) 2020 Societe francaise de radiologie. Published by Elsevier Masson SAS. All rights reserved

A decision support system for vine growers based on a bayesian network

We propose here a decision support system for vine growers to assess the quality of a vineyard to be planted. The quality of a vineyard is defined by the probability of possible profitability of the wine sales he is able to produce. The model, based on a Bayesian network (BN), takes into account environment and the parameters defining vineyard status with their associated interactions. BN are widely used for knowledge representation and reasoning under uncertainty in natural resource management. There is a rising interest in BN as tools for ecological and agronomic modelling. Data were collected from knowledge of vine-growing experts. We developed a C# computer program predicting the likely quality of a vineyard. The model has been validated on existing vineyards with prediction ability around 75%. This system should ease assessments of the likely impact of the choices and decisions of vine growers on the quality of new vineyards to be planted in any part of the world. No such model has been developed before for vine growers