Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 168 for “"Density estimation"”.
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Manifold aligned density estimation
… thesis is mainly concerned with manifold aligned density estimation problems. In particular, the work presented in this thesis includes efficiently learning the density distribution on very large-scale datasets and estimating the manifold aligned density through explicit manifold modeling. First, …
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Density Estimation for Robust Financial Econometrics
… distance between two nonparametrically smoothed density estimates: the simulated model density and corresponding observed density. This approach generalizes work of Beran (1977) and Basu and Lindsay (1994) so that dependent data and simulated model densities are allowed, enabling the estimation …
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Density estimation with Kullback-Leibler loss
Probability density functions are estimated by the method of maximum likelihood in sequences of regular exponential families. The approximation families of log-densities that we consider are polynomials, splines, and trigonometric series. Bounds on the relative entropy (Kullback-Leibler number) …
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Some topics in sequential density estimation
… be i.i.d random variables with common unknown density function f. Here we are interested in estimating the unknown density f with bounded Mean Integrated Absolute Error (MIAE). Devroye and Gyorfi (1985) obtained asymptotic bounds for the MIAE in estimating f by a kernel estimate $\ f\sb{n}.$ …
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Hierarchical density estimation for image classification
… In this thesis, we present a novel hierarchical density estimation approach for image classification. This new approach partitions the feature space into small regions using a tree structure. For each region, ""local"" distribution is characterized by class-conditional Gaussians via hierarchical …
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A Mixture-based Framework for Nonparametric Density Estimation
… a mixture-based framework for nonparametric density estimation. This framework advocates the use of a mixture model with a nonparametric mixing distribution to approximate the distribution of the data. The implementation of a mixture-based nonparametric density estimator generally requires …
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Density estimation and some topics in multivariate analysis
… statistic for the standardized bivariate normal density. Attempts to fit a four-parameter generalized gamma density function to this empirical distribution were only partially successful. Part II, entitled "The centroid method of numerical integration", begins with a discussion of the often …
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Non-asymptotic bounds for prediction problems and density estimation.
This dissertation investigates the learning scenarios where a high-dimensional parameter has to be estimated from a given sample of fixed size, often smaller than the dimension of the problem. The first part answers some open questions for the binary classification problem in the framework of …
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Spectrum sensing based on capon power spectral density estimation
… sensing based on the Capon Power Spectral Density (PSD) estimation method. The proposed method estimates the received PSD, and uses it to identify free and busy channels. A cooperative spectrum sensing approach is also introduced. The goal is to solve the common hidden node problem and help …
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Practical Aspects Of Kernel Smoothing For Binary Regression And Density Estimation
… smoothing in three areas: binary regression, density estimation and Poisson regression sample size calculations. Both nonparametric and semiparametric binary regression estimators are examined in detail, and extended to two bandwidth cases. The asymptotic behaviour of these estimators is …
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Denoising by wavelet thresholding using multivariate minimum distance partial density estimation
… need not be specified. We use a new technique in density estimation which minimizes an distance criterion (L2E) to estimate the parameters of the partial density that represents the noise component. The L2E estimate for the weight of the noise component, w&d4;L2E , determines the fraction of …
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Advances in Symbolic Regression: From Generalized Formulation to Density Estimation and Inverse Problem
… PDE discovery. Furthermore, we introduce MESSY Estimation, a Maximum-Entropy based Stochastic and Symbolic densitY estimation method. The proposed approach infers probability density functions symbolically from samples by leveraging the Maximum Entropy Distribution (MED) principle. We uncover …
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Mean Hellinger Distance as an Error Criterion in Univariate and Multivariate Kernel Density Estimation
… the bandwidth matrix for multivariate kernel density estimation. More recently other criteria have been advocated as competitors to the MISE, such as the mean absolute error. In this study we define a weighted version of the Hellinger distance for multivariate densities and show that it has an …
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A STUDY OF PROJECTION PURSUIT METHODS (MULTIVARIATE STATISTICS, DIMENSION REDUCTION, DENSITY ESTIMATION, GRAPHICS, ENTROPY)
… are assumed to be a sample from a population density then it is natural to measure the information content in a projection by evaluating the Shannon entropy or the Fisher information of the marginal density corresponding to the projection. Because the population density is an unknown the …
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