Jean-Pierre Ottoy

Limitations of short-term experiments designed for identification of activated sludge biodégradation models by fast dynamic phenomena

Abstract Experimental results obtained in a batch reactor are presented showing different fast dynamic phenomena. All measured OUR-profiles show a ‘start-up’ phase upon substrate addition. Neither the response time of the DO-electrode, the mixing characteristics in the reactor or the extracellular transport limitations could explain this behaviour. It is hypothesised that intracellular transport and conversion processes are responsible for the transient response. Second, dynamics induced by regulation processes of the macromolecular cell composition could be observed. Finally, the adaptation of the mixed culture population to changed operating conditions is demonstrated. The implications for modelling activated sludge biodegradation with simple structured models identified from such experiments are discussed.

Extreme value statistics: potential benefits in water quality management

Recently extreme value statistics have proven useful in environmental applications like the assessment of sealevels, wind speeds and ozone concentrations. In this paper, after a brief overview of the statistical theory of extreme values, modelling issues are discussed with stress on applications in water quality management. Risk analysis procedures are presented that consider the extremal behaviour of water quality in the design stage of environmental constructions.

A collection of applets for visualizing statistical concepts

This paper describes a set of didactic tools for statistical teaching, implemented as JAVA applets. The tools allow to visualize a number of statistical concepts, and to experiment with them interactively.

EMLasso: logistic lasso with missing data

In clinical settings, missing data in the covariates occur frequently. For example, some markers are expensive or hard to measure. When this sort of data is used for model selection, the missingness is often resolved through a complete case analysis or a form of single imputation. An alternative sometimes comes in the form of leaving the most damaged covariates out. All these strategies jeopardise the goal of model selection. In earlier work, we have applied the logistic Lasso in combination with multiple imputation to obtain results in such settings, but we only provided heuristic arguments to advocate the method. In this paper, we propose an improved method that builds on firm statistical arguments and that is developed along the lines of the stochastic expectation-maximisation algorithm. We show that our method can be used to handle missing data in both categorical and continuous predictors, as well as in a nonpenalised regression. We demonstrate the method by applying it to data of 273 lung cancer patients. The objective is to select a model for the prediction of acute dysphagia, starting from a large set of potential predictors, including clinical and treatment covariates as well as a set of single-nucleotide polymorphisms.

An extension of the Wilcoxon-Mann-Whitney test for analyzing RT-qPCR data.

Abstract Classical approaches for analyzing reverse transcription quantitative polymerase chain reaction (RT-qPCR) data commonly require normalization before assessing differential expression (DE). Normalization often has a substantial effect on the interpretation and validity of the subsequent analysis steps, but at the same time it causes a reduction in variance and introduces dependence among the normalized outcomes. These effects can be substantial, however, they are typically ignored. Most normalization techniques and methods for DE focus on mean expression and are sensitive to outliers. Moreover, in cancer studies, for example, oncogenes are often only expressed in a subsample of the populations during sampling. This primarily affects the skewness and the tails of the distribution and the mean is therefore not necessarily the best effect size measure within these experimental setups. In our contribution, we propose an extension of the Wilcoxon-Mann-Whitney test which incorporates a robust normalization, and the uncertainty associated with normalization is propagated into the final statistical summaries for DE. Our method relies on semiparametric regression models that focus on the probability P{Y≤Y′}, where Y and Y′ denote independent responses for different subject groups. This effect size is robust to outliers, while remaining informative and intuitive when DE affects the shape of the distribution instead of only the mean. We also extend our approach for assessing DE for multiple features simultaneously. Simulation studies show that the test has a good performance, and that it is very competitive with standard methods for this platform. The method is illustrated on two neuroblastoma studies.

Some generalizations of the Anderson-Darling statistic

The Anderson-Darling statistic is basically a weighted average of Pearson statistics. In this paper, we first propose to use other weights and next we generalize the Anderson-Darling statistic by inserting the Cressie-and-Read family of statistics into the Anderson-Darling statistic.

A Nonparametric Test for Independence Based on Sample Space Partitions

Abstract In this paper, a class of nonparametric tests for independence between two continuous random variables is proposed. The members of the class are characterized by the size of the partitions of the sample space on which the test statistics are based. The test statistic that corresponds to the smallest partition size may be seen as an extension of Hoeffdings statistic with an Anderson–Darling-type weight function. In this simplest case, we have proven its asymptotic null distribution as well as its omnibus consistency. In a simulation study, the powers of the new tests are compared to those of some other tests for independence. It is concluded that overall high powers are obtained with these new tests.

Regional residual plots for assessing the fit of linear regression models

An intuitively appealing lack-of-fit test to assess the adequacy of a regression model is introduced together with a graphical diagnostic tool. The graphical method itself includes a formal testing procedure, and, it is particularly useful to detect the location of lack-of-fit. The procedure is based on regional residuals, using subsets of the space of the independent variables. A simulation study shows that, the proposed procedures in simple linear regression have similar power as those of some popular classical lack-of-fit tests. In case of local departures from the hypothesized regression model, the new tests are shown to be more powerful. Therefore, when it becomes difficult to discriminate between systematic deviations and noise, regional residual plots are very helpful in formally locating areas of lack-of-fit in the predictor space. Data examples illustrate the ability of the new methods to detect and to locate lack-of-fit.

An extension of the Anderson–Darling k-sample test to arbitrary sample space partition sizes

In this paper we first show that the k-sample Anderson–Darling test is basically an average of Pearson statistics in 2u2009×u2009k contingency tables that are induced by observation-based partitions of the sample space. As an extension, we construct a family of rank test statistics, indexed by cu2009∈u2009ℕ, which is based on similarly constructed cu2009×u2009k partitions. An extensive simulation study, in which we compare the new test with others, suggests that generally very high powers are obtained with the new tests. Finally we propose a decomposition of the test statistic in interpretable components.

Data management of river water quality data: A semi-automatic procedure for data validation

[1]xa0Monitoring networks typically generate large amounts of data. Before the data can be added to the database, they have to be validated. In this paper, a semi-automatic procedure is presented to validate river water quality data. On the basis of historical data, additive models are established to predict new observations and to construct prediction intervals (PIs). A new observation is accepted if it is located in the interval. The coverage of the prediction intervals and its power to detect anomalous data are assessed in a simulation study. The method is illustrated on two case studies in which the method detected abnormal nitrate concentrations in the water body provoked by a dry summer which was followed by an extreme winter period. The case studies also show that similar to classical multivariate outlier detection tools, the semi-automatic procedure allows the detection of suspicious observations lying at the edges as well as observations lying at the center of the univariate distribution of the observations, but, without having to impose linear relationships typically associated with these classical methods.