Search results for "estimation"
showing 10 items of 924 documents
Estimating the Demand for New Destinations for a Regional Airport Based on Its Catchment Area
2020
Abstract Estimating accurately demand both at market and company level for specific goods and services can be considered a necessity for every organization. Traditional demand estimation methods may not be relevant for estimating the demand for new destinations to be introduced by a regional airport. The present paper proposes to fill this gap and to develop the demand estimation literature by presenting a novel demand estimation method. Two research objectives are developed in this regard: (1) determining the catchment area of Sibiu International Airport (a regional airport in Romania) for destinations operated by competitor airports and not by Sibiu International Airport and (2) estimatin…
Probabilistic cross-validation estimators for Gaussian process regression
2018
Gaussian Processes (GPs) are state-of-the-art tools for regression. Inference of GP hyperparameters is typically done by maximizing the marginal log-likelihood (ML). If the data truly follows the GP model, using the ML approach is optimal and computationally efficient. Unfortunately very often this is not case and suboptimal results are obtained in terms of prediction error. Alternative procedures such as cross-validation (CV) schemes are often employed instead, but they usually incur in high computational costs. We propose a probabilistic version of CV (PCV) based on two different model pieces in order to reduce the dependence on a specific model choice. PCV presents the benefits from both…
Regional income inequality in France 1860–1954: Methods and findings
2019
This paper explores regional (departement or NUTS3) income inequality in France between 1860 and 1954. To this end we first document the existing evidence, evaluate the estimation methods and findi...
A posteriori modelling-discretization error estimate for elliptic problems with L ∞-Coefficients
2017
We consider elliptic problems with complicated, discontinuous diffusion tensor A0. One of the standard approaches to numerically treat such problems is to simplify the coefficient by some approximation, say Aϵ, and to use standard finite elements. In [19] a combined modelling-discretization strategy has been proposed which estimates the discretization and modelling errors by a posteriori estimates of functional type. This strategy allows to balance these two errors in a problem adapted way. However, the estimate of the modelling error was derived under the assumption that the difference A0 - Aϵ becomes small with respect to the L∞-norm. This implies in particular that interfaces/discontinui…
Crop Yield Estimation and Interpretability With Gaussian Processes
2021
This work introduces the use of Gaussian processes (GPs) for the estimation and understanding of crop development and yield using multisensor satellite observations and meteo- rological data. The proposed methodology combines synergistic information on canopy greenness, biomass, soil, and plant water content from optical and microwave sensors with the atmospheric variables typically measured at meteorological stations. A com- posite covariance is used in the GP model to account for varying scales, nonstationary, and nonlinear processes. The GP model reports noticeable gains in terms of accuracy with respect to other machine learning approaches for the estimation of corn, wheat, and soybean …
Modélisation du comportement des agriculteurs face au risque dans un modèle de programmation mathématique positive (PMP) à grande échelle
2017
Agricultural production is characterized for being a risky business due to weather variability, market instability, plant diseases as well as climate change and political economy uncertainty. The modelling of risk at farm level is not new, however, the inclusion of risk in Positive Mathematical Programming (PMP) models is particularly challenging. Most of the few existing PMP-risk approaches have been conducted at farm-type level and for a very limited and specific sample of farms. This implies that the modelling of risk and uncertainty at individual farm level and in a large scale system is still a challenging task. The aim of this paper is to formulate, estimate and test a robust methodol…
Random resampling numerical simulations applied to a SEIR compartmental model
2021
AbstractIn this paper, we apply resampling techniques to a modified compartmental SEIR model which takes into account the existence of undetected infected people in an epidemic. In particular, we implement numerical simulations for the evolution of the first wave of the COVID-19 pandemic in Spain in 2020. We show, by using suitable measures of goodness, that the point estimates obtained by the bootstrap samples improve the ones of the original data. For example, the relative error of detected currently infected people is equal to 0.061 for the initial estimates, while it is reduced to 0.0538 for the mean over all bootstrap estimated series.
The European COVID-19 drugs calculation tool: an aid for the estimation of the drugs needed during the SARS-CoV 2 pandemic
2021
Objective To create an informatics supportive tool, which can assist healthcare professionals in estimating potential requirements for essential drug supplies to respond to the current SARS-CoV-2 pandemic based on epidemiological forecasting. Methods The tool was based on a Susceptible-Infected-Removed (SIR) epidemiological model in which the population is divided into three compartments and transmission parameters are specified to define the rate at which people move between stages. Appropriate data entry was guaranteed by the creation of structured guided paths. The drugs needed for the forecasted patients were estimated according to a list of critical care drugs compiled by consulting pr…
High Quality Reconstruction of Dynamic Objects using 2D-3D Camera Fusion
2017
International audience; In this paper, we propose a complete pipeline for high quality reconstruction of dynamic objects using 2D-3D camera setup attached to a moving vehicle. Starting from the segmented motion trajectories of individual objects, we compute their precise motion parameters, register multiple sparse point clouds to increase the density, and develop a smooth and textured surface from the dense (but scattered) point cloud. The success of our method relies on the proposed optimization framework for accurate motion estimation between two sparse point clouds. Our formulation for fusing it closest-point and it consensus based motion estimations, respectively in the absence and pres…
SAMSLAM: Simulated Annealing Monocular SLAM
2013
This paper proposes a novel monocular SLAM approach. For a triplet of successive keyframes, the approach inteleaves the registration of the three 3D maps associated to each image pair in the triplet and the refinement of the corresponding poses, by progressively limiting the allowable reprojection error according to a simulated annealing scheme. This approach computes only local overlapping maps of almost constant size, thus avoiding problems of 3D map growth. It does not require global optimization, loop closure and back-correction of the poses.