Search results for "Density estimation"

showing 10 items of 61 documents

Multi-scale Modelling of Segmentation

2016

While listening to music, people often unwittingly break down musical pieces into constituent chunks such as verses and choruses. Music segmentation studies have suggested that some consensus regarding boundary perception exists, despite individual differences. However, neither the effects of experimental task (i.e., real-time vs. annotated segmentation), nor of musicianship on boundary perception are clear. Our study assesses musicianship effects and differences between segmentation tasks. We conducted a real-time experiment to collect segmentations by musicians and nonmusicians from nine musical pieces. In a second experiment on non-real-time segmentation, musicians indicated boundaries a…

Speech recognitionmedia_common.quotation_subject05 social sciencesKernel density estimationBoundary (topology)Pulse (music)Scale (music)050105 experimental psychologyTask (project management)03 medical and health sciences0302 clinical medicinePerception0501 psychology and cognitive sciencesActive listeningSegmentationPsychology030217 neurology & neurosurgeryMusicmedia_commonMusic Perception
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Linear and nonlinear experimental regimes of stochastic resonance

2000

We investigate the stochastic resonance phenomenon in a physical system based on a tunnel diode. The experimental control parameters are set to allow the control of the frequency and amplitude of the deterministic modulating signal over an interval of values spanning several orders of magnitude. We observe both a regime described by the linear response theory and the nonlinear deviation from it. In the nonlinear regime we detect saturation of the power spectral density of the output signal detected at the frequency of the modulating signal and a dip in the noise level of the same spectral density. When these effects are observed we detect a phase and frequency synchronization between the st…

Statistical Mechanics (cond-mat.stat-mech)Stochastic resonanceSpectral densitySpectral density estimationFOS: Physical sciencesSignalSynchronization (alternating current)Nonlinear systemAmplitudeOrders of magnitude (time)Control theoryStatistical physicsCondensed Matter - Statistical MechanicsMathematics
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Markov Chain Monte Carlo Methods for High Dimensional Inversion in Remote Sensing

2004

SummaryWe discuss the inversion of the gas profiles (ozone, NO3, NO2, aerosols and neutral density) in the upper atmosphere from the spectral occultation measurements. The data are produced by the ‘Global ozone monitoring of occultation of stars’ instrument on board the Envisat satellite that was launched in March 2002. The instrument measures the attenuation of light spectra at various horizontal paths from about 100 km down to 10–20 km. The new feature is that these data allow the inversion of the gas concentration height profiles. A short introduction is given to the present operational data management procedure with examples of the first real data inversion. Several solution options for…

Statistics and Probability010504 meteorology & atmospheric sciencesAttenuationInversion (meteorology)Markov chain Monte CarloDensity estimationInverse problem01 natural sciencesOccultation010104 statistics & probabilitysymbols.namesakeMetropolis–Hastings algorithmStatisticsPrior probabilitysymbols0101 mathematicsStatistics Probability and UncertaintyAlgorithm0105 earth and related environmental sciencesMathematicsJournal of the Royal Statistical Society Series B: Statistical Methodology
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Testing Goodness-of-Fit with the Kernel Density Estimator: GoFKernel

2015

To assess the goodness-of-fit of a sample to a continuous random distribution, the most popular approach has been based on measuring, using either L∞ - or L2 -norms, the distance between the null hypothesis cumulative distribution function and the empirical cumulative distribution function. Indeed, as far as I know, almost all the tests currently available in R related to this issue (ks.test in package stats, ad.test in package ADGofTest, and ad.test, ad2.test, ks.test, v.test and w2.test in package truncgof) use one of these two distances on cumulative distribution functions. This paper (i) proposes dgeometric.test, a new implementation of the test that measures the discrepancy between a s…

Statistics and ProbabilityCumulative distribution functionKernel density estimationProbability density functionKolmogorov–Smirnov testEmpirical distribution functionsymbols.namesakeGoodness of fitStatisticssymbolsStatistics Probability and UncertaintyNull hypothesisRandom variablelcsh:Statisticslcsh:HA1-4737SoftwareMathematicsJournal of Statistical Software
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Weighted samples, kernel density estimators and convergence

2003

This note extends the standard kernel density estimator to the case of weighted samples in several ways. In the first place I consider the obvious extension by substituting the simple sum in the definition of the estimator by a weighted sum, but I also consider other alternatives of introducing weights, based on adaptive kernel density estimators, and consider the weights as indicators of the informational content of the observations and in this sense as signals of the local density of the data. All these ideas are shown using the Penn World Table in the context of the macroeconomic convergence issue.

Statistics and ProbabilityEconomics and EconometricsMathematical optimizationKernel density estimationEstimatorMultivariate kernel density estimationKernel principal component analysisMathematics (miscellaneous)Penn World TableKernel embedding of distributionsVariable kernel density estimationKernel (statistics)Applied mathematicsSocial Sciences (miscellaneous)MathematicsEmpirical Economics
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Local bandwidth selection for kernel density estimation in a bifurcating Markov chain model

2020

International audience; We propose an adaptive estimator for the stationary distribution of a bifurcating Markov Chain onRd. Bifurcating Markov chains (BMC for short) are a class of stochastic processes indexed by regular binary trees. A kernel estimator is proposed whose bandwidths are selected by a method inspired by the works of Goldenshluger and Lepski [(2011), 'Bandwidth Selection in Kernel Density Estimation: Oracle Inequalities and Adaptive Minimax Optimality',The Annals of Statistics3: 1608-1632). Drawing inspiration from dimension jump methods for model selection, we also provide an algorithm to select the best constant in the penalty. Finally, we investigate the performance of the…

Statistics and ProbabilityKernel density estimationadaptive estimationNonparametric kernel estimation01 natural sciences010104 statistics & probability[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST]0502 economics and businessbinary treesApplied mathematicsbifurcating autoregressive processes0101 mathematics[MATH]Mathematics [math]050205 econometrics MathematicsBinary treeStationary distributionMarkov chainStochastic processModel selection05 social sciencesEstimator[MATH.MATH-PR]Mathematics [math]/Probability [math.PR]Adaptive estimatorStatistics Probability and UncertaintyGoldenshluger-Lepski methodology
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Gamma Kernel Intensity Estimation in Temporal Point Processes

2011

In this article, we propose a nonparametric approach for estimating the intensity function of temporal point processes based on kernel estimators. In particular, we use asymmetric kernel estimators characterized by the gamma distribution, in order to describe features of observed point patterns adequately. Some characteristics of these estimators are analyzed and discussed both through simulated results and applications to real data from different seismic catalogs.

Statistics and ProbabilityNonparametric statisticsEstimatorKernel principal component analysisPoint processVariable kernel density estimationKernel embedding of distributionsModeling and SimulationKernel (statistics)Bounded domainStatisticsGamma distributionGamma kernel estimatorIntensity functionTemporal point processes.Settore SECS-S/01 - StatisticaMathematicsCommunications in Statistics - Simulation and Computation
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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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A Random Field Approach to Transect Counts of Wildlife Populations

1991

Line transect counting of a wildlife population is considered a sampling from a planar marked point process, where the marks describe the detectability of the animals. Sampling properties of transect counts and a new density estimator are derived from a counting process, which is a shot-noise field induced by the marked point process. A general formula for the sampling variance of a transect is derived and applied to compare five common types of transects. Some stereological connections of transect sampling and density estimators are shown.

Statistics and Probabilityeducation.field_of_studyRandom fieldCounting processCovariance functionPopulationSampling (statistics)EstimatorGeneral MedicineDensity estimationStatisticsStatistics Probability and UncertaintyeducationTransectMathematicsBiometrical Journal
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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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