Shahin Tavakoli

Shahin Tavakoli
University of Geneva | UNIGE · Institute of Statistics (ISTAT)

About

16
Publications
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289
Citations

Publications

Publications (16)
Preprint
The sound of our speech is influenced by the places we come from. Great Britain contains a wide variety of distinctive accents which are of interest to linguistics. In particular, the "a" vowel in words like "class" is pronounced differently in the North and the South. Speech recordings of this vowel can be represented as formant curves or as Mel-f...
Article
Full-text available
Prediction of subject age from brain anatomical MRI has the potential to provide a sensitive summary of brain changes, indicative of different neurodegenerative diseases. However, existing studies typically neglect the uncertainty of these predictions. In this work we take into account this uncertainty by applying methods of functional data analysi...
Preprint
Full-text available
Prediction of subject age from brain anatomical MRI has the potential to provide a sensitive summary of brain changes, indicative of different neurodegenerative diseases. However, existing studies typically neglect the uncertainty of these predictions. In this work we take into account this uncertainty by applying methods of functional data analysi...
Article
Dialect variation is of considerable interest in linguistics and other social sciences. However, traditionally it has been studied using proxies (transcriptions) rather than acoustic recordings directly. We introduce novel statistical techniques to analyze geolocalized speech recordings and to explore the spatial variation of pronunciations continu...
Preprint
Full-text available
In this paper, we set up theoretical foundations for high-dimensional functional factor models for the analysis of large panels of functional time series (FTS). We first establish a representation result stating that if the first $r$ eigenvalues of the covariance operator of the cross-section of $N$ FTS are unbounded as $N$ diverges and if the $(r+...
Article
The assumption of separability of the covariance operator for a random image or hypersurface can be of substantial use in applications, especially in situations where the accurate estimation of the full covariance structure is unfeasible, either for computational reasons, or due to a small sample size. However, inferential tools to verify this assu...
Chapter
We consider the problem of testing for separability in nonparametric covariance operators of multidimensional functional data is considered. We cast the problem in a tensor product of Hilbert space framework, where the role of the partial trace operator is emphasized, and the tests proposed are computationally tractable. An applications to acoustic...
Article
The book Mr. Tickle is a childhood favourite for many. But this fantastical story of a man with “extraordinary long arms” is helping statisticians derive a more realistic understanding of the differences in regional accents. By Marius A. Tirlea, Shahin Tavakoli, Davide Pigoli and John A. D. Aston
Article
Full-text available
Dialect variation is of considerable interest in linguistics and other social sciences. However, traditionally it has been studied using proxies (transcriptions) rather than acoustic recordings directly. We introduce novel statistical techniques to analyse geolocalised speech recordings and to explore the spatial variation of pronunciations continu...
Article
Motivated by the problem of inferring the molecular dynamics of DNA in solution, and linking them with its base-pair composition, we consider the problem of comparing the dynamics of functional time series (FTS), and of localizing any inferred differences in frequency and along curvelength. The approach we take is one of Fourier analysis, where the...
Article
Full-text available
The assumption of separability of covariance operators in the different directions for a random image or hypersurface can be of substantial use in applications, especially in situations where the accurate estimation of the full covariance structure is unfeasible, either for computational reasons or due to a small sample size. However, inferential t...
Article
This work is about time series of functional data (functional time series), and consists of three main parts. In the first part (Chapter 2), we develop a doubly spectral decomposition for functional time series that generalizes the Karhunen–Loève expansion. In the second part (Chapter 3), we develop the theory of estimation for the spectral density...
Article
We develop a doubly spectral representation of a stationary functional time series, and study the properties of its empirical version. The representation decomposes the time series into an integral of uncorrelated frequency components (Cramér representation), each of which is in turn expanded in a Karhunen–Loève series. The construction is based on...
Article
Full-text available
We develop the basic building blocks of a frequency domain framework for drawing statistical inferences on the second-order structure of a stationary sequence of functional data. The key element in such a context is the spectral density operator, which generalises the notion of a spectral density matrix to the functional setting, and characterises...

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