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- Irène Gannaz
- 2006

This paper is concerned with a semiparametric partially linear regression model with unknown regression coefficients, an unknown nonparametric function for the non-linear component, and unobservable Gaussian distributed random errors. We present a wavelet thresholding based estimation procedure to estimate the components of the partial linear model by… (More)

- Irène Gannaz
- Statistics and Computing
- 2007

This paper is concerned with a semiparametric partially linear regression model with unknown regression coefficients, an unknown nonparametric function for the non-linear component, and unobservable Gaussian distributed random errors. We present a wavelet thresholding based estimation procedure to estimate the components of the partial linear model by… (More)

- Irene Gannaz
- 2014

We want to analyse EEG recordings in order to investigate the phonemic categorization at a very early stage of auditory processing. This problem can be modelled by a supervised classification of functional data. Discrimination is explored via a logistic functional linear model, using a wavelet representation of the data. Different procedures are… (More)

Multivariate processes with long-range dependent properties are found in a large number of applications including finance, geophysics and neuroscience. For real data applications, the correlation between time series is crucial. Usual estimations of correlation can be highly biased due to phase-shifts caused by the differences in the properties of… (More)

- Irène Gannaz
- 2013

The paper deals with generalized functional regression. The aim is to estimate the influence of covariates on observations, drawn from an exponential distribution. The link considered has a semiparametric expression: if we are interested in a functional influence of some covariates, we authorize others to be modeled linearly. We thus consider a generalized… (More)

- Irène Gannaz
- 2013

The paper deals with a generalized linear model with functional data using a wavelet representation of the signals. A reduction of dimension is first obtained through a principal component analysis. The discriminative function is then given by a loglikelihood maximization, with a LASSO penalization, in order to ensure the sparsity of the wavelet… (More)

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