Stochastic modelling using large data sets : applications in ecology and genetics

Abstract : There are two main parts in this thesis. The first one concerns valvometry, which is here the study of the distance between both parts of the shell of an oyster, over time. The health status of oysters can be characterized using valvometry in order to obtain insights about the quality of their environment. We consider that a renewal process with four states underlies the behaviour of the studied oysters. Such a hidden process can be retrieved from a valvometric signal by assuming that some probability density function linked with this signal, is bimodal. We then compare several estimators which take this assumption into account, including kernel density estimators.In another chapter, we compare several regression approaches, aiming at analysing transcriptomic data. To understand which explanatory variables have an effect on gene expressions, we apply a multiple testing procedure on these data, through the linear model FAMT. The SIR method may find nonlinear relations in such a context. It is however more commonly used when the response variable is univariate. A multivariate version of SIR was then developed. Procedures to measure gene expressions can be expensive. The sample size n of the corresponding datasets is then often small. That is why we also studied SIR when n is less than the number of explanatory variables p.
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Submitted on : Wednesday, September 25, 2013 - 11:32:28 AM
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Raphaël Coudret. Stochastic modelling using large data sets : applications in ecology and genetics. General Mathematics [math.GM]. Université Sciences et Technologies - Bordeaux I, 2013. English. ⟨NNT : 2013BOR14838⟩. ⟨tel-00865867⟩



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