Search results for "Statistical Model"
showing 10 items of 163 documents
Likelihood approach to the first dark matter results from XENON100
2011
Many experiments that aim at the direct detection of Dark Matter are able to distinguish a dominant background from the expected feeble signals, based on some measured discrimination parameter. We develop a statistical model for such experiments using the Profile Likelihood ratio as a test statistic in a frequentist approach. We take data from calibrations as control measurements for signal and background, and the method allows the inclusion of data from Monte Carlo simulations. Systematic detector uncertainties, such as uncertainties in the energy scale, as well as astrophysical uncertainties, are included in the model. The statistical model can be used to either set an exclusion limit or …
Enhanced transport-related air pollution prediction through a novel metamodel approach
2017
Abstract This research proposes a novel approach to improve the ability to forecast low frequency extreme events of transport-related pollution in urban areas using a limited input data set. The approach is based on the idea of a self-managing model, able to adapt to unexpected changes in pollution level. In more detail, for a given combination of variables, it selects the most suitable prediction model within a set of alternative air quality models, estimated for a wider range of locations and conditions. In this study, the new approach is tested for the prediction of nitrogen dioxide concentration in the United Kingdom (UK), specifically in an air quality monitoring site of the Greater Ma…
Polynomial Regression and Measurement Error
2020
Many of the phenomena of interest in information systems (IS) research are nonlinear, and it has consequently been recognized that by applying linear statistical models (e.g., linear regression), we may ignore important aspects of these phenomena. To address this issue, IS researchers are increasingly applying nonlinear models to their datasets. One popular analytical technique for the modeling and analysis of nonlinear relationships is polynomial regression, which in its simplest form fits a "U-shaped" curve to the data. However, the use of polynomial regression can be problematic when the independent variables are contaminated with measurement error, and the implications of error can be m…
A probabilistic framework for automatic prostate segmentation with a statistical model of shape and appearance
2011
International audience; Prostate volume estimation from segmented prostate contours in Trans Rectal Ultrasound (TRUS) images aids in diagnosis and treatment of prostate diseases, including prostate cancer. However, accurate, computationally efficient and automatic segmentation of the prostate in TRUS images is a challenging task owing to low Signal-To-Noise-Ratio (SNR), speckle noise, micro-calcifications and heterogeneous intensity distribution inside the prostate region. In this paper, we propose a probabilistic framework for propagation of a parametric model derived from Principal Component Analysis (PCA) of prior shape and posterior probability values to achieve the prostate segmentatio…
Local softening of information geometric indicators of chaos in statistical modeling in the presence of quantum-like considerations
2013
In a previous paper (C. Cafaro et al., 2012), we compared an uncorrelated 3D Gaussian statistical model to an uncorrelated 2D Gaussian statistical model obtained from the former model by introducing a constraint that resembles the quantum mechanical canonical minimum uncertainty relation. Analysis was completed by way of the information geometry and the entropic dynamics of each system. This analysis revealed that the chaoticity of the 2D Gaussian statistical model, quantified by means of the Information Geometric Entropy (IGE), is softened or weakened with respect to the chaoticity of the 3D Gaussian statistical model due to the accessibility of more information. In this companion work, we…
Précipitations et relief en Afrique orientale et australe : modélisations statistiques et géostatistiques.
2008
By considering two examples in Eastern and Southern Africa, this work has two aims: a better understanding of the influence of topography on the spatial distribution of rainfall and an optimal interpolation of station rainfall data, taking into account topography. To this end, an original methodology is developped, partly derived from previous studies focusing on extratropical regions.First, a statistical model is defined. With the help of a multi-scalar decomposition of topographical information into descriptors, a multiple linear regression is performed. This model is used to better understand the relationship between rainfall and topography. In Eastern Africa, the spatial distribution of…
Quantifying biochemical reaction rates from static population variability within complex networks
2021
Quantifying biochemical reaction rates within complex cellular processes remains a key challenge of systems biology even as high-throughput single-cell data have become available to characterize snapshots of population variability. That is because complex systems with stochastic and non-linear interactions are difficult to analyze when not all components can be observed simultaneously and systems cannot be followed over time. Instead of using descriptive statistical models, we show that incompletely specified mechanistic models can be used to translate qualitative knowledge of interactions into reaction rate functions from covariability data between pairs of components. This promises to tur…
Regularization operators for natural images based on nonlinear perception models.
2006
Image restoration requires some a priori knowledge of the solution. Some of the conventional regularization techniques are based on the estimation of the power spectrum density. Simple statistical models for spectral estimation just take into account second-order relations between the pixels of the image. However, natural images exhibit additional features, such as particular relationships between local Fourier or wavelet transform coefficients. Biological visual systems have evolved to capture these relations. We propose the use of this biological behavior to build regularization operators as an alternative to simple statistical models. The results suggest that if the penalty operator take…
A population density grid for Spain
2013
This article describes a high-resolution land cover data set for Spain and its application to dasymetric population mapping (at census tract level). Eventually, this vector layer is transformed into a grid format. The work parallels the effort of the Joint Research Centre (JRC) of the European Commission, in collaboration with Eurostat and the European Environment Agency (EEA), in building a population density grid for the whole of Europe, combining CORINE Land Cover with population data per commune. We solve many of the problems due to the low resolution of CORINE Land Cover, which are especially visible with Spanish data. An accuracy assessment is carried out from a simple aggregation of …
A comparative analysis of different spatial sampling schemes: Modelling of SSRB data
2008
Low spatial resolution satellite sensors provide information over relatively large targets with typical pixel resolutions of hundreds of km2. However, the spatial scales of ground measurements are usually much smaller. Such differences in spatial scales makes the interpretation of comparisons between quantities derived from low resolution sensors and ground measurements particularly difficult. It also highlights the importance of developing appropriate sampling strategies when designing ground campaigns for validation studies of low resolution sensors. We make use of statistical modelling of high resolution surface shortwave radiation budget (SSRB) data to look into this problem. A spatial …