Search results for "Software"

showing 10 items of 7396 documents

Outlier detection with automatic modelling: TRAMO/SEATS versus X-12-ARIMA

2012

Statistics and Probabilitybusiness.industryComputer scienceApplied MathematicsModeling and SimulationPattern recognitionAnomaly detectionData miningArtificial intelligenceAutoregressive integrated moving averagecomputer.software_genrebusinesscomputerModel Assisted Statistics and Applications
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What subject matter questions motivate the use of machine learning approaches compared to statistical models for probability prediction?

2014

This is a discussion of the following papers: "Probability estimation with machine learning methods for dichotomous and multicategory outcome: Theory" by Jochen Kruppa, Yufeng Liu, Gerard Biau, Michael Kohler, Inke R. Konig, James D. Malley, and Andreas Ziegler; and "Probability estimation with machine learning methods for dichotomous and multicategory outcome: Applications" by Jochen Kruppa, Yufeng Liu, Hans-Christian Diener, Theresa Holste, Christian Weimar, Inke R. Konig, and Andreas Ziegler.

Statistics and Probabilitybusiness.industryProbability estimationStatistical modelGeneral MedicineMachine learningcomputer.software_genreLogistic regressionMulticategoryOutcome (probability)Subject matterDienerEconometricsArtificial intelligenceStatistics Probability and UncertaintybusinesscomputerMathematicsBiometrical Journal
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cglasso: An R Package for Conditional Graphical Lasso Inference with Censored and Missing Values

2023

Sparse graphical models have revolutionized multivariate inference. With the advent of high-dimensional multivariate data in many applied fields, these methods are able to detect a much lower-dimensional structure, often represented via a sparse conditional independence graph. There have been numerous extensions of such methods in the past decade. Many practical applications have additional covariates or suffer from missing or censored data. Despite the development of these extensions of sparse inference methods for graphical models, there have been so far no implementations for, e.g., conditional graphical models. Here we present the general-purpose package cglasso for estimating sparse co…

Statistics and Probabilityconditional Gaussian graphical modelscglasso conditional Gaussian graphical models glasso high-dimensionality sparsity censoring missing dataglassosparsityhigh-dimensionalityconditional Gaussian graphical models glasso high-dimensionality sparsity censoring missing datacglassomissing datacensoringStatistics Probability and UncertaintySettore SECS-S/01 - StatisticaSoftware
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Efficient change point detection in genomic sequences of continuous measurements

2010

Abstract Motivation: Knowing the exact locations of multiple change points in genomic sequences serves several biological needs, for instance when data represent aCGH profiles and it is of interest to identify possibly damaged genes involved in cancer and other diseases. Only a few of the currently available methods deal explicitly with estimation of the number and location of change points, and moreover these methods may be somewhat vulnerable to deviations of model assumptions usually employed. Results: We present a computationally efficient method to obtain estimates of the number and location of the change points. The method is based on a simple transformation of data and it provides re…

Statistics and Probabilitymodel selectionBreast Neoplasmscomputer.software_genreBiochemistryCell LineSimple (abstract algebra)Cell Line TumorHumansComputer Simulationpiecewise constant modelMolecular BiologyMathematicsOligonucleotide Array Sequence AnalysisSupplementary dataComparative Genomic HybridizationModels StatisticalSeries (mathematics)Model selectionGenomicsComputer Science ApplicationsComputational MathematicsR packageTransformation (function)Computational Theory and MathematicsChange pointsChangepointaCGH analysiFemaleData miningSettore SECS-S/01 - StatisticacomputerChange detection
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Contributed discussion on article by Pratola

2016

The author should be commended for his outstanding contribution to the literature on Bayesian regression tree models. The author introduces three innovative sampling approaches which allow for efficient traversal of the model space. In this response, we add a fourth alternative.

Statistics and Probabilitymodel selectionMarkov Chain Monte Carlo (MCMC)Bayesian regression treeComputer scienceBig dataBayesian regression tree (BRT) modelsComputingMilieux_LEGALASPECTSOFCOMPUTINGbirth–death processMachine learningcomputer.software_genreSequential Monte Carlo methods01 natural sciencespopulation Markov chain Monte Carlo010104 statistics & probabilitysymbols.namesakebig data0502 economics and businessBayesian Regression Trees (BART)0101 mathematics050205 econometrics Bayesian treed regressionMultiple Try Metropolis algorithmsINFERÊNCIA ESTATÍSTICAbusiness.industryApplied MathematicsModel selection05 social sciencesRejection samplingData scienceVariable-order Bayesian networkTree (data structure)Tree traversalMarkov chain Monte Carlocontinuous time Markov processsymbolsArtificial intelligencebusinessBayesian linear regressioncommunication-freecomputerGibbs samplingBayesian Analysis
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Systematic handling of missing data in complex study designs : experiences from the Health 2000 and 2011 Surveys

2016

We present a systematic approach to the practical and comprehensive handling of missing data motivated by our experiences of analyzing longitudinal survey data. We consider the Health 2000 and 2011 Surveys (BRIF8901) where increased non-response and non-participation from 2000 to 2011 was a major issue. The model assumptions involved in the complex sampling design, repeated measurements design, non-participation mechanisms and associations are presented graphically using methodology previously defined as a causal model with design, i.e. a functional causal model extended with the study design. This tool forces the statistician to make the study design and the missing-data mechanism explicit…

Statistics and Probabilitymultiple imputationComputer sciencecomputer.software_genre01 natural sciences010104 statistics & probability03 medical and health sciences0302 clinical medicinenon-responseSampling design030212 general & internal medicine0101 mathematicsCausal modelta112Clinical study designInverse probability weightingSampling (statistics)non-participationMissing dataData sciencedoubly robust methodsSurvey data collectionData miningStatistics Probability and Uncertaintycomputerinverse probability weightingStatisticiancausal model with designJournal of Applied Statistics
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A semiparametric approach to estimate reference curves for biophysical properties of the skin

2006

Reference curves which take one covariable into account such as the age, are often required in medicine, but simple systematic and efficient statistical methods for constructing them are lacking. Classical methods are based on parametric fitting (polynomial curves). In this chapter, we describe a new methodology for the estimation of reference curves for data sets, based on nonparametric estimation of conditional quantiles. The derived method should be applicable to all clinical or more generally biological variables that are measured on a continuous quantitative scale. To avoid the curse of dimensionality when the covariate is multidimensional, a new semiparametric approach is proposed. Th…

Statistics::TheoryKernel density estimationcomputer.software_genre01 natural sciences010104 statistics & probability0502 economics and businessCovariateSliced inverse regressionApplied mathematicsStatistics::MethodologySemiparametric regression0101 mathematics[SHS.ECO] Humanities and Social Sciences/Economics and Finance050205 econometrics MathematicsParametric statisticsDimensionality reduction05 social sciencesNonparametric statistics[ SDV.SPEE ] Life Sciences [q-bio]/Santé publique et épidémiologie[SHS.ECO]Humanities and Social Sciences/Economics and Finance3. Good health[SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie[SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologieC140;C630Data miningcomputerQuantile
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Consistent device simulation model describing perovskite solar cells in steady-state, transient, and frequency domain

2019

​This document is the Accepted Manuscript version of a Published Work that appeared in final form in ACS Applied Materials & Interfaces, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see https://pubs.acs.org/doi/10.1021/acsami.9b04991

Steady state (electronics)Materials scienceIMPSImpedance spectroscopy610 Medicine & health02 engineering and technology010402 general chemistrycomputer.software_genre01 natural sciencesChemical societyGeneral Materials ScienceTransient (computer programming)Device simulation10266 Clinic for Reconstructive SurgeryMaterials621.3: Elektrotechnik und ElektronikCèl·lules fotoelèctriquesTrapsPerovskite (structure)Drift-diffusion modelingProgramming languagePerovskite solar cellsHysteresis021001 nanoscience & nanotechnology2500 General Materials Science0104 chemical sciencesMobile ionsFrequency domainTransient photo-current0210 nano-technologycomputer
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Steady-state and tracking analysis of a robust adaptive filter with low computational cost

2007

This paper analyses a new adaptive algorithm that is robust to impulse noise and has a low computational load [E. Soria, J.D. Martin, A.J. Serrano, J. Calpe, and J. Chambers, A new robust adaptive algorithm with low computacional cost, Electron. Lett. 42 (1) (2006) 60-62]. The algorithm is based on two premises: the use of the cost function often used in independent component analysis and a fuzzy modelling of the hyperbolic tangent function. The steady-state error and tracking capability of the algorithm are analysed using conservation methods [A. Sayed, Fundamentals of Adaptive Filtering, Wiley, New York, 2003], thus verifying the correspondence between theory and experimental results.

Steady stateComputational complexity theoryAdaptive algorithmFunction (mathematics)Tracking (particle physics)Impulse noiseIndependent component analysisAdaptive filterControl and Systems EngineeringControl theorySignal ProcessingComputer Vision and Pattern RecognitionElectrical and Electronic EngineeringSoftwareMathematicsSignal Processing
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3-D shape reconstruction in an active stereo vision system using genetic algorithms

2003

Abstract The recovery of 3-D shape information (depth) using stereo vision analysis is one of the major areas in computer vision and has given rise to a great deal of literature in the recent past. The widely known stereo vision methods are the passive stereo vision approaches that use two cameras. Obtaining 3-D information involves the identification of the corresponding 2-D points between left and right images. Most existing methods tackle this matching task from singular points, i.e. finding points in both image planes with more or less the same neighborhood characteristics. One key problem we have to solve is that we are on the first instance unable to know a priori whether a point in t…

Stereo camerasbusiness.industryComputer scienceMachine visionEpipolar geometry3D reconstructionComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONlaw.inventionStereopsisProjectorArtificial IntelligencelawSignal ProcessingComputer visionComputer Vision and Pattern RecognitionArtificial intelligenceFundamental matrix (computer vision)businessSoftwareComputer stereo visionStereo cameraPattern Recognition
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