Search results for "Statistic"
showing 10 items of 12520 documents
Identification of Objects Based on Generalized Amplitude-Phase Images Statistical Models
2017
The article presents the dynamical objects identification technology based on statistical models of amplitude-phase images (APIm) – multidimensional data arrays (semantic models) and statistical correlation analysis methods using the generalized discrete Hilbert transforms (DHT) – 2D Hilbert (Foucault) isotropic (HTI), anisotropic (HTA) and total transforms – AP-analysis (APA) to calculate the APIm. The identified objects are modeled with 3D airplanes templates rotated in space around the center of Cartesian coordinate system. The DHT domain system of coordinates displaying the plane projections (2D flat images) remains to be space-invariant. That causes the anisotropic properties of APIm a…
VARIABLE SELECTION FOR NOISY DATA APPLIED IN PROTEOMICS
2014
International audience; The paper proposes a variable selection method for pro-teomics. It aims at selecting, among a set of proteins, those (named biomarkers) which enable to discriminate between two groups of individuals (healthy and pathological). To this end, data is available for a cohort of individuals: the biological state and a measurement of concentrations for a list of proteins. The proposed approach is based on a Bayesian hierarchical model for the dependencies between biological and instrumental variables. The optimal selection function minimizes the Bayesian risk, that is to say the selected set of variables maximizes the posterior probability. The two main contributions are: (…
Abnormal Textures Identification Based on Digital Hilbert Optics Methods: Fundamental Transforms and Models
2017
The article presents the abnormal textures identification technology based on structural and statistical models of amplitude-phase images (APIm) – multidimensional data arrays (semantic models) and statistical correlation analysis methods using the generalized discrete Hilbert transforms (DHT) – 2D Hilbert (Foucault) isotropic (HTI), anisotropic (HTA) and total transforms – AP-analysis (APA) to calculate the APIm. The identified fragments of textures are obtained as examples of experimental observation of real mammograms contains areas of pathological tissues. The DHT based information technology as conceptual chart description is discussed and illustrated with DHO domain images. As additio…
Modeling Energy Demand Aggregators for Residential Consumers
2013
International audience; Energy demand aggregators are new actors in the energy scenario: they gather a group of energy consumers and implement a demand- response paradigm. When the energy provider needs to reduce the current energy demand on the grid, it can pay the energy demand aggregator to reduce the load by turning off some of its consumers loads or postponing their activation. Currently this operation involves only greedy energy consumers like industrial plants. In this paper we want to study the potential of aggregating a large number of small energy consumers like home users as it may happen in smart grids. In particular we want to address the feasibility of such approach by conside…
Nonlinear statistical retrieval of surface emissivity from IASI data
2017
Emissivity is one of the most important parameters to improve the determination of the troposphere properties (thermodynamic properties, aerosols and trace gases concentration) and it is essential to estimate the radiative budget. With the second generation of infrared sounders, we can estimate emissivity spectra at high spectral resolution, which gives us a global view and long-term monitoring of continental surfaces. Statistically, this is an ill-posed retrieval problem, with as many output variables as inputs. We here propose nonlinear multi-output statistical regression based on kernel methods to estimate spectral emissivity given the radiances. Kernel methods can cope with high-dimensi…
An Artificial Bee Colony Approach for Classification of Remote Sensing Imagery
2018
This paper presents a novel Artificial Bee Colony (ABC) approach for supervised classification of remote sensing images. One proposes to apply an ABC algorithm to optimize the coefficients of the set of polynomial discriminant functions. We have experimented the proposed ABC-based classifier algorithm for a Landsat 7 ETM+ image database, evaluating the influence of the ABC model parameters on the classifier performances. Such ABC model parameters are: numbers of employed/onlooker/scout bees, number of epochs, and polynomial degree. One has compared the best ABC classifier Overall Accuracy (OA) with the performances obtained using a set of benchmark classifiers (NN, NP, RBF, and SVM). The re…
Conditional Versus Joint Probability Assessments
1984
AbstractThe assessment of conditional and / or joint probabilities of events that constitute scenarios is necessary for sound planning, forecasting, and decision making. The assessment process is complex and subtle, and various difficulties are encountered in the elicitation of such probabilities such as, implicit violations ofthe probability calculus and some meaningfjilness conditions. The necessary and sufficient as well as meaningfulness conditions that the elicited information on conditional and joint probabilities must satisfy are evaluated against actual assessments empirically. A high frequency of violation of these conditions was observed in assessing both conditional and joint pro…
A Novel Intelligent Technique for Product Acceptance Process Optimization on the Basis of Misclassification Probability in the Case of Log-Location-S…
2019
In this paper, to determine the optimal parameters of the product acceptance process under parametric uncertainty of underlying models, a new intelligent technique for optimization of product acceptance process on the basis of misclassification probability is proposed. It allows one to take into account all possible situations that may occur when it is necessary to optimize the product acceptance process. The technique is based on the pivotal quantity averaging approach (PQAA) which allows one to eliminate the unknown parameters from the problem and to use available statistical information as completely as possible. It is conceptually simple and easy to use. One of the most important featur…
Exploring relationships between grid cell size and accuracy for debris-flow susceptibility models: a test in the Giampilieri catchment (Sicily, Italy)
2016
Debris flows are among the most hazardous phenomena in nature, requiring the preparation of suscep- tibility models in order to cope with this severe threat. The aim of this research was to verify whether a grid cell-based susceptibility model was capable of predicting the debris- flow initiation sites in the Giampilieri catchment (10 km2), which was hit by a storm on the 1st October 2009, resulting in more than one thousand landslides. This kind of event is to be considered as recurrent in the area as attested by historical data. Therefore, predictive models have been prepared by using forward stepwise binary logistic regression (BLR), a landslide inventory and a set of geo- environmental …
Intelligent Constructing Exact Tolerance Limits for Prediction of Future Outcomes Under Parametric Uncertainty
2021
The problem of constructing one-sided exact statistical tolerance limits on the kth order statistic in a future sample of m observations from a distribution of log-location-scale family on the basis of an observed sample from the same distribution is considered. The new technique proposed here emphasizes pivotal quantities relevant for obtaining tolerance factors and is applicable whenever the statistical problem is invariant under a group of transformations that acts transitively on the parameter space. The exact tolerance limits on order statistics associated with sampling from underlying distributions can be found easily and quickly making tables, simulation, Monte Carlo estimated percen…