Publications des institutions partenaires
Direct Simultaneous Inference in Additive Models and its Application to Model Undernutrition
Institution partenaire
English / 01/01/2012
Kernel Smoothers and Bootstrapping for Semiparametric Mixed Effects Models
Institution partenaire
English / 01/01/2012
The Semiparametric Juhn-Murphy- Pierce Decomposition of the Gender Pay Gap with an application to Spain
Institution partenaire
English / 01/01/2012
A Prediction Divergence Criterion for Model Selection
In this paper, we propose a new criterion for selection between nested models. We suppose that the correct model is one (or near one) of the available models and construct a criterion which is based on the Bregman divergence between the out-of-sample prediction of the smaller model and the in-sample prediction of the larger model. This criterion, the prediction divergence criterion (…
Institution partenaire
English / 01/01/2012
A Framework for Inertial Sensor Calibration using Complex Stochastic Error Models, in the proceedings of the Position Location and Navigation Symposium (PLANS), 2012 IEEE/ION
Modeling and estimation of gyroscope and accelerometer errors is generally a very challenging task, especially for low-cost inertial MEMS sensors whose systematic errors have complex spectral structures. Consequently, identifying correct error-state parameters in a INS/GNSS Kalman filter/smoother becomes difficult when several processes are superimposed. In such situations, the…
Institution partenaire
English / 01/01/2012
Robust filtering
Filtering methods are powerful tools to estimate the hidden state of a statespace model from observations available in real time. However, they are known to be highly sensitive to the presence of small misspecifications of the underlying model and to outliers in the observation process. In this paper, we show that the methodology of robust statistics can be adapted to sequential…
Institution partenaire
English / 01/01/2012
Trends in the Gender Pay Gap in Spain: A Semiparametric Analysis
Institution partenaire
English / 01/01/2012
Fast Robust Model Selection in Large Datasets
Large datasets are more and more common in many research fields. In particular, in the linear regression context, it is often the case that a huge number of potential covariates are available to explain a response variable, and the first step of a reasonable statistical analysis is to reduce the number of covariates. This can be done in a forward selection procedure that includes the…
Institution partenaire
English / 01/01/2011
Isotone additive latent variable models
For manifest variables with additive noise and for a given number of latent variables with an assumed distribution, we propose to nonparametrically estimate the association between latent and manifest variables. Our estimation is a two step procedure: first it employs standard factor analysis to estimate the latent variables as theoretical quantiles of the assumed distribution;…
Institution partenaire
English / 01/01/2011
Constrained Expectation-Maximization Algorithm for Stochastic Inertial Error Modeling: Study of Feasibility
Stochastic modeling is a challenging task for low-cost sensors which errors can have complex spectral structures. This makes the tuning process of the INS/GNSS Kalman filter often sensitive and diffcult. For example, first-order Gauss-Markov processes are very often used in inertial sensor models. But the estimation of their parameters is a non-trivial task if the error structure is…
Institution partenaire
English / 01/01/2011
Generalized Method of Wavelet Moments
This paper presents a new estimation method for the parameters of a model generating times series. Given some conditions on the form of the power spectral density associated to the process, it is possible to indirectly recover pa- rameter estimates from wavelet variances (WV) associated to the process. We propose an optimization criterion based on a standardized distance between the…
Institution partenaire
English / 01/01/2011
Statistical modelling of radio audience data: a parametric approach
In this thesis, we develop models to analyze zero-inflated truncated heavy-tailed dependent data to fit radio audience data in Switzerland. These models are able to explain, by means of covariates, both the probability of observing a positive outcome and the mean of the positive outcomes, which respectively correspond to the audience indicators of reach and of time spent listening.…
Institution partenaire
English / 01/01/2011
Robust VIF Regression
The sophisticated and automated means of data collection used by an increasing number of institutions and companies leads to extremely large datasets. Subset selection in regression is essential when a huge number of covariates can potentially explain a response variable of interest. The recent statistical literature has seen an emergence of new selection methods that provide some…
Institution partenaire
English / 01/01/2011
Explaining grassland biomass - the contribution of biodiversity and climate depends on fertilisation and mowing frequency
Institution partenaire
English / 01/01/2011
Economic Aspects and Social Drivers of Land Degradation
Institution partenaire
English / 01/01/2011
Improving Modeling of MEMS-IMUs Operating in GNSS-denied Conditions
Stochastic modeling is a challenging task for lowcost inertial sensors whose errors can have complex spectral structures. This makes the tuning process of the INS/GNSS Kalman filter often sensitive and difficult. We are currently investigating two approaches for bounding the errors in the mechanization. The first is an improved modeling of stochastic errors through the superposition…
Institution partenaire
English / 01/01/2011
Barycentric Bounds in Stochastic Programming : Theory and Application
The design and analysis of efficient approximation schemes is of fundamental importance in stochastic programming research. Bounding approximations are particularly popular for providing strict error bounds that can be made small by using partitioning techniques. In this article we develop a powerful bounding method for linear multistage stochastic programs with a generalized…
Institution partenaire
English / 01/01/2011
Acute appendicitis: prospective evaluation of a diagnostic algorithm integrating ultrasound and low-dose CT to reduce the need of standard CT.
To evaluate an algorithm integrating ultrasound and low-dose unenhanced CT with oral contrast medium (LDCT) in the assessment of acute appendicitis, to reduce the need of conventional CT.
Institution partenaire
English / 01/01/2011
Modeling the rigidity of client rate for non-maturing accounts
Institution partenaire
English / 28/08/2010
The Size Problem of Bootstrap Tests when the Null is Non- or Semiparametric
Institution partenaire
English / 01/01/2010
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