Our work aims at facilitating the schedulability analysis of non-critical systems, in particular those that have soft real-time constraints, where WCETs can be replaced by less stringent probabilistic bounds, which we call Maximal Execution Times (METs). In our approach, we can obtain adequate probabilistic execution time models by separating the non-random input data dependency from a modeling...

Fitting statistical models is a widely employed technique for the segmentation of medical images. While this approach gives impressive results for simple structures, shape models are often not flexible enough to accurately represent complex shapes. We present a fitting approach, which increases the model fitting accuracy without requiring a larger training data-set. Inspired by a local regressi...

The present research work outlines the main ideas behind statistical regression by a 2-independent-variates and 1-dependent-variate model based on the invariance of measures in probabilistic spaces. The principle of probabilistic measure invariance, applied under the assumption that the model be isotonic, leads to a system of differential equations. Such differential system is reformulated in t...

In regression diagnostics, the case deletion model (CDM) and the mean shift outlier model (MSOM) are commonly used in practice. In this paper we show that the estimates of CDM and MSOM are equal in a wide class of statistical models, which include LSE, MLE, Bayesian estimate and M-estimate in linear and nonlinear regression models; MLE in generalized linear models and exponential family nonline...

This paper describes some statistical analyses of a particular archaeological material (pottery) originating at some sites in the city of Tours. An important part of the archaeological study of pottery is the comparison of ceramic assemblages to establish the absolute dates of contexts. In this paper, a statistical model is built to assess this comparison. The statistical procedure uses classic...

In many studies we wish to assess how a range of variables are associated with a particular outcome and also determine the strength of such relationships so that we can begin to understand how these factors relate to each other at a population level. Ultimately, we may also be interested in predicting the outcome from a series of predictive factors available at, say, a routine clinic visit. In ...

Many impact studies require climate change information at a finer resolution than that provided by Global Climate Models (GCMs). In the last 10 years, downscaling techniques, both dynamical (i.e. Regional Climate Model) and statistical methods, have been developed to obtain fine resolution climate change scenarios. In this study, an automated statistical downscaling (ASD) regression-based appro...

Regression analysis is a statistical tool for the investigation of relationships between variables. Usually, the investigator seeks to ascertain the causal effect of one variable upon another—the effect of a price increase upon demand, for example, or the effect of changes in the money supply upon the inflation rate. To explore such issues, the investigator assembles data on the underlying vari...

After the recent death of Peter Gould, I decided to look once again at his insightful paper`Is Statistix Inferens the Geographical Name for a Wild Goose?' (Gould 1970) ± hence the title for this guest editorial. For those readers who have not seen this article, Gould outlines a number of shortcomings of the common statistical practices of geographers of the day. Many of these relate to the assu...