Data Analysis; Statistics; Classification; Big Data; Spacial Statistics; Time Series; Computational Methods
Khamma Thulasi Ram, Zhang Yuming, Guerrier Stéphane, Boubekri Mohamed (2020), Generalized additive models: An efficient method for short-term energy prediction in office buildings, in Energy
, 213, 118834-118834.
XuHaotian, ZhangYuming, Guerrier Stéphane, Jurado Juan, Khaghani Mehran, Bakalli Gaetan, Karemera Mucyo, Molinari Roberto, Orso Samuel, Raquet John, Schubert Christine, Skaloud Jan (2020), Wavelet-Based Moment-Matching Techniques for Inertial Sensor Calibration, in IEEE Transactions on Instrumentation and Measurement
, 69(10), 7542-7551.
Lachance James C, Radhakrishnan Sridhar, Madiwale Gaurav, Guerrier Stéphane, Vanamala Jairam KP (2020), Targeting hallmarks of cancer with a food system-based approach, in Nutrition
, 69, 110563-110563.
Wang Yi “Victor”, Gardoni Paolo, Murphy Colleen, Guerrier Stéphane (2020), Worldwide predictions of earthquake casualty rates with seismic intensity measure and socioeconomic data: a fragility-based formulation, in Natural hazards review
, 21(2), 04020001-04020001.
Radi Ahmed, Bakalli Gaetan, Guerrier Stéphane, El-Sheimy Naser, Sesay Abu B, Molinari Roberto (2019), A multisignal wavelet variance-based framework for inertial sensor stochastic error modeling, in IEEE Transactions on Instrumentation and Measurement
, 68(12), 4924-4936.
Xu Haotian, Guerrier Stéphane, Molinari Roberto Carlo, Karemera Mucyo (2019), Multivariate signal modeling with applications to inertial sensor calibration, in IEEE Transactions on Signal Processing
, 67(19), 5143-5152.
Wang Yi, Gardoni Paolo, Murphy Colleen, Guerrier Stéphane (2019), Predicting fatality rates due to earthquakes accounting for community vulnerability, in Earthquake spectra
, 35(2), 513-536.
GuerrierStéphane, Dupuis-LozeronElise, MaYanyuan, Victoria-FeserMaria-Pia (2019), Simulation-Based Bias Correction Methods for Complex Models, in Journal of the American Statistical Association (Theory & Methods)
Wang Yi, Gardoni Paolo, Murphy Colleen, Guerrier Stéphane, Empirical Predictive Modeling Approach to Quantifying Social Vulnerability to Natural Hazards, in Annals of the American Association of Geographers
JammalamadakaSreenivasa, GuerrierStephane, MangalamVasudevan, Exact Distributions and Performance of some Two-sample Nonparametric Tests for Circular Data, in Sankhya B
HeerahSachin, MolinariRoberto, StephaneGuerrier, Marshall-ColonAmy, Granger-Causal Testing for Irregularly Sampled Time Series with Application to Nitrogen Signaling in Arabidopsis, in Bioinformatics
Guerrier Stéphane, MolinariRoberto, Victoria-FeserMaria-Pia, XuHaotian, Robust Two-Step Wavelet-Based Inference for Time Series Models, in Journal of the American Statistical Association (Theory & Methods)
As the collection of data grows in size and complexity, one important aspect of scientific research lies in finding patterns or signals hidden in massive amounts of data that are of relevance to problems that need to be tackled in practice. Given the size of the problems, there is also a need to carry out this procedure in a computationally efficient manner and, more importantly, using sound statistical methods. Indeed, the fast growing production and gathering of data, at least indirectly, produces problems whose complexity grows at an equivalent speed and therefore new efficient statistical methods for proper data analysis become unavoidably necessary. In many cases, the complexity of the considered models and the relative numerical challenges entailed by them make the currently available statistical methods not viable, without considering their possible extensions which would be equally not sustainable. In the project I propose, I intend to contribute to the development of new computationally efficient statistical methods for the analysis of dependent data (in time and space) and model selection. The computational efficiency will be achieved by proposing simplified statistical methods which, remaining numerically tractable, will preserve desirable statistical properties with very little loss in terms of statistical efficiency. Moreover, I also intend to develop statistical methods that can directly take into account the expertise of scientists within a general theoretical framework, thereby attempting to avoid consequent data analyses that are performed sequentially, often leading to significant losses of information from one step to the subsequent one. This research project will be completed by the development of software and computational tools for a direct access to the research output on behalf of users from academia, public sector or private sector.