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Catégorie

Robustesse (statistiques)

Publications associées (8)

Dataset for the evaluation of student-level outcomes of a primary school Computer Science curricular reform

Francesco Mondada, Barbara Bruno, Jessica Elke Dehler Zufferey, Laila Abdelsalam El-Hamamsy

The associated peer reviewed article that will appear in the International Journal of STEM education : El-Hamamsy, L., Bruno, B., Audrin, C., Chevalier, M., Avry S., Dehler Zufferey, J., and Mondada, F. (2023). How are Primary School Computer Science Curri ...

Zenodo2023

Comparison of Three Imputation Methods for Groundwater Level Timeseries

Andrea Rinaldo

This study compares three imputation methods applied to the field observations of hydraulic head in subsurface hydrology. Hydrogeological studies that analyze the timeseries of groundwater elevations often face issues with missing data that may mislead bot ...

MDPI2023

Predicting involuntary hospitalization in psychiatry: A machine learning investigation

Mehdi Gholam

Background Coercion in psychiatry is a controversial issue. Identifying its predictors and their interaction using traditional statistical methods is difficult, given the large number of variables involved. The purpose of this study was to use machine-lear ...

CAMBRIDGE UNIV PRESS2021

Structural identification methods for full-scale bridges

Ian Smith, James-Alexandre Goulet, Irwanda Laory

Results of two doctoral theses, one using a model-based approach and the other using model-free methods, are summarized. Both included case studies of full-scale bridges. While model-based approaches are appropriate for individual infrastructure-management ...

2013

Sparse Iterative Closest Point

Mark Pauly, Sofien Bouaziz, Andrea Tagliasacchi

Rigid registration of two geometric data sets is essential in many applications, including robot navigation, surface reconstruction, and shape matching. Most commonly, variants of the Iterative Closest Point (ICP) algorithm are employed for this task. Thes ...

2013

Spectral modeling of time series with missing data

Singular spectrum analysis is a natural generalization of principal component methods for time series data. In this paper we propose an imputation method to be used with singular spectrum-based techniques which is based on a weighted combination of the for ...

Elsevier2013

System identification via nuclear norm regularization for simulated moving bed processes from incomplete data sets

Colin Neil Jones

The application of nuclear norm regularization to system identification was recently shown to be a useful method for identifying low order linear models. In this paper, we consider nuclear norm regularization for identification of simulated moving bed proc ...

IEEE2009

Resamping variance estimation in surveys with missing data

Anthony Christopher Davison, Sylvain Sardy

We discuss variance estimation by resampling in surveys in which data are missing. We derive a formula for jackknife linearization in the case of calibrated estimation with deterministic regression imputation, and compare the resulting variance estimates w ...

2007