Search results for "Conditional"

showing 10 items of 294 documents

Parameter orthogonality and conditional profile likelihood: the exponential power function case

1999

Orthogonality, according to Fisher’s metrics, between the parameters of a probability density function, as well as giving rise to a series of statistical implications, makes it possible to express a function of conditional profile likelihood with better properties than the ordinary profile likelihood function. In the present paper the parameters of exponential power function are made orthogonal and the conditional profile likelihood of the shape parameter p is determined in order to study its properties with reference to p estimation. Moreover, by means of a simulation plan, a comparison is made between the estimates of p obtained from the conditional profile log-likelihood and those obtain…

Statistics and ProbabilityStatisticsApplied mathematicsProbability density functionDensity estimationConditional probability distributionLikelihood functionLikelihood principleConditional varianceShape parameterExponential functionMathematicsCommunications in Statistics - Theory and Methods
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Selecting the tuning parameter in penalized Gaussian graphical models

2019

Penalized inference of Gaussian graphical models is a way to assess the conditional independence structure in multivariate problems. In this setting, the conditional independence structure, corresponding to a graph, is related to the choice of the tuning parameter, which determines the model complexity or degrees of freedom. There has been little research on the degrees of freedom for penalized Gaussian graphical models. In this paper, we propose an estimator of the degrees of freedom in $$\ell _1$$ -penalized Gaussian graphical models. Specifically, we derive an estimator inspired by the generalized information criterion and propose to use this estimator as the bias term for two informatio…

Statistics and ProbabilityStatistics::TheoryKullback–Leibler divergenceKullback-Leibler divergenceComputer scienceGaussianInformation Criteria010103 numerical & computational mathematicsModel complexityModel selection01 natural sciencesTheoretical Computer Science010104 statistics & probabilitysymbols.namesakeStatistics::Machine LearningGeneralized information criterionEntropy (information theory)Statistics::MethodologyGraphical model0101 mathematicsPenalized Likelihood Kullback-Leibler Divergence Model Complexity Model Selection Generalized Information Criterion.Model selectionEstimatorStatistics::ComputationComputational Theory and MathematicsConditional independencesymbolsPenalized likelihoodStatistics Probability and UncertaintySettore SECS-S/01 - StatisticaAlgorithmStatistics and Computing
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Simulation of BSDEs with jumps by Wiener Chaos Expansion

2016

International audience; We present an algorithm to solve BSDEs with jumps based on Wiener Chaos Expansion and Picard's iterations. This paper extends the results given in Briand-Labart (2014) to the case of BSDEs with jumps. We get a forward scheme where the conditional expectations are easily computed thanks to chaos decomposition formulas. Concerning the error, we derive explicit bounds with respect to the number of chaos, the discretization time step and the number of Monte Carlo simulations. We also present numerical experiments. We obtain very encouraging results in terms of speed and accuracy.

Statistics and ProbabilityWiener Chaos expansionDiscretizationMonte Carlo methodTime stepConditional expectation01 natural sciences010104 statistics & probabilitybackward stochastic differential equations with jumpsFOS: MathematicsApplied mathematics60H10 60J75 60H35 65C05 65G99 60H070101 mathematicsMathematicsPolynomial chaosApplied MathematicsNumerical analysis010102 general mathematicsMathematical analysista111Probability (math.PR)numerical methodCHAOS (operating system)[MATH.MATH-PR]Mathematics [math]/Probability [math.PR]Modeling and SimulationScheme (mathematics)Mathematics - Probability
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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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Estimation of total electricity consumption curves by sampling in a finite population when some trajectories are partially unobserved

2019

International audience; Millions of smart meters that are able to collect individual load curves, that is, electricity consumption time series, of residential and business customers at fine scale time grids are now deployed by electricity companies all around the world. It may be complex and costly to transmit and exploit such a large quantity of information, therefore it can be relevant to use survey sampling techniques to estimate mean load curves of specific groups of customers. Data collection, like every mass process, may undergo technical problems at every point of the metering and collection chain resulting in missing values. We consider imputation approaches (linear interpolation, k…

Statistics and Probabilityconstructionkernel smoothingPopulationSurvey samplingimputation01 natural sciences010104 statistics & probability[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST]0502 economics and businessStatisticsImputation (statistics)0101 mathematicseducationsurvey samplingfunctional data050205 econometrics Mathematicsconfidence bandsConsumption (economics)Estimationeducation.field_of_studymissing completely at randombusiness.industry05 social sciencesprincipal analysis by conditional estimationSampling (statistics)[MATH.MATH-PR]Mathematics [math]/Probability [math.PR]nearest neighboursKernel smoothervariance-estimationElectricityStatistics Probability and Uncertaintybusinessvariance approximation
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Statistics of residence time for Lévy flights in unstable parabolic potentials

2020

We analyze the residence time problem for an arbitrary Markovian process describing nonlinear systems without a steady state. We obtain exact analytical results for the statistical characteristics of the residence time. For diffusion in a fully unstable potential profile in the presence of Lévy noise we get the conditional probability density of the particle position and the average residence time. The noise-enhanced stability phenomenon is observed in the system investigated. Results from numerical simulations are in very good agreement with analytical ones.

Steady stateSettore FIS/02 - Fisica Teorica Modelli E Metodi Matematicinoise-enhanced stability nonlinear relaxation time stochastic processes Lévy noiseMarkov process01 natural sciencesStability (probability)010305 fluids & plasmasNonlinear systemsymbols.namesakeLévy flight0103 physical sciencessymbolsConditional probability densityStatistical physicsDiffusion (business)010306 general physicsResidence time (statistics)Mathematics
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A MULTISTAGE DECISION MODEL FOR THE OPTIMAL ISSUANCE OF SOVEREIGN DEBT UNDER ESA95

2014

The aim of this paper is to develop a stochastic programming model for the optimal composition of debt portfolios. Such a prob- lem has recently acquired a more and more major interest, being the indebtedness of many countries quite worrying. We propose a stochastic programming model where the decision maker desires to minimize a certain cost function while bounding the interest rate risk. Our analysis focus mainly on the cost function ESA95, which is a methodology developed by the European System of Accounts to gauge the cost of servicing the debt. The model is implemented under two financing strategies, one as- sumes the government cannot resort to budget surplus to pay interest expenses,…

Stocastich ProgrammingSettore SECS-S/06 -Metodi Mat. dell'Economia e d. Scienze Attuariali e Finanz.Debt ManagementConditional Value-at-Risk
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Firm Size and Volatility Analysis in the Spanish Stock Market

2011

In this article, three strongly related questions are studied. First, volatility spillovers between large and small firms in the Spanish stock market are analyzed by using a conditional CAPM with an asymmetric multivariate GARCH-M covariance structure. Results show that there exist bidirectional volatility spillovers between both types of firms, especially after bad news. Second, the volatility feedback hypothesis explaining the volatility asymmetry feature is investigated. Results show significant evidence for this hypothesis. Finally, the study uncovers that conditional beta coefficient estimates within the used model are insensitive to sign and size asymmetries in the unexpected shock re…

Stochastic volatilityFinancial economicsRisk premiumAutoregressive conditional heteroskedasticityEconomics Econometrics and Finance (miscellaneous)CovarianceImplied volatilityVolatility risk premiumMultivariate garchPrice of riskVolatility swapEconomicsEconometricsForward volatilityVolatility smileCapital asset pricing modelStock marketVolatility (finance)SSRN Electronic Journal
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Graphical Models for Dependencies and Associations

1992

The role of graphical representations is described in distinguishing various special forms of independency structure that can arise with multivariate data, especially in observational studies in the social sciences. Conventions for constructing the graphs and strategies for analysing three sets of data are summarized. Finally some directions for desirable future work are outlined.

Structure (mathematical logic)Multivariate statisticsTheoretical computer scienceWork (electrical)Conditional independenceComputer scienceObservational studyGraphical model
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Reliability of Short-Term Heart Rate Variability Indexes Assessed through Photoplethysmography

2018

The gold standard method to monitor heart rate variability (HRV) comprises measuring the time series of interbeat interval durations from electrocardiographic (ECG) recordings. However, due to the widespread use, simplicity and usability of photoplethysmographic (PPG) techniques, monitoring pulse rate variability (PRV) from pulse wave recordings has become a viable alternative to standard HRV analysis. The present study investigates the accuracy of PRV, measured as a surrogate of HRV, for the quantification of descriptive indexes computed in the time domain (mean, variance), frequency domain (low-to-high frequency power ratio LF/HF, HF band central frequency) and information domain (entropy…

Supine positionEntropy0206 medical engineeringBiomedical EngineeringHealth Informatics02 engineering and technologySettore ING-INF/01 - ElettronicaRobust regressionElectrocardiography03 medical and health sciences0302 clinical medicineHeart RatePhotoplethysmogramStatisticsHumansHeart rate variabilityTime domainPhotoplethysmographyMathematicsConditional entropyReproducibility of Results020601 biomedical engineeringFrequency domainSignal ProcessingSettore ING-INF/06 - Bioingegneria Elettronica E Informatica030217 neurology & neurosurgeryInterbeat interval
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