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 137 for “"kernel density"”.
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On Variable Bandwidth Kernel Density And Regression Estimation
We study the ideal variable bandwidth kernel density estimator introduced by McKay (1993) and the plug-in practical version of the variable bandwidth kernel density estimator with two sequences of bandwidths as in Ginè and Sang (2013).We estimate the variance of the variable bandwidth kernel …
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Some Improvement on Convergence Rates of Kernel Density Estimator
… focuses on improving the convergence rates of kernel density estimators. Firstly, a bias reduced kernel density estimator is introduced and investigated. In order to reduce bias, we intuitively subtract an estimated bias term from ordinary kernel density estimator. Theoretical properties such …
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Mean Hellinger Distance as an Error Criterion in Univariate and Multivariate Kernel Density Estimation
… choosing 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 …
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A high-order Legendre-WENO numerical scheme for the non-conservative kernel density equations modeling particle-laden flows
A numerical scheme is developed to integrate the kernel density equations for the particle phase of disperse flows. The kernel density equations arise from least squares minimization of the approximation of the position-velocity phase-space particle distribution by a sum of kernel density …
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Least squares mixture decomposition estimation
… Estimator (LSMDE) is a new nonparametric density estimation technique developed by modifying the ordinary kernel density estimators. While the ordinary kernel density estimator assumes equal weight (l/<i>n</i>) for each data point, LSMDE assigns the optimized weight to each data point via …
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Spatial Analysis of Fatal Automobile Crashes in Kentucky
… points and average daily traffic flow; 2) planar kernel density estimation of FARS crash events based on temporal and demographic attributes within the data; and 3) two case studies using network kernel density estimation along roadways to determine hot spots fatal crashes in Jefferson County and …
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Reassessing the Ranging Behavior of Black-And-White Ruffed Lemurs (Varecia variegata) in Ranomafana National Park, Madagascar
… space use and movement using autocorrelated kernel density estimation (AKDE), a new methodology available from the continuous-time movement model (ctmm) package in R. Data were collected from 24 adults and subadults (10 males, 11 females, 3 subadult males) living in two adjacent <em>V. …
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A Probabilistic Approach To Multiple-Instance Learning
… of instances. In the second approach, embedded kernel density estimation (ekde), bags are represented in an instance induced (very high dimensional) space. A parametric stochastic neighbor embedding method is applied to learn a mapping that projects bags into a 2-d or 1-d space. Class …
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Essays in econometrics
… centers. Chapters 2 and 3 extend univariate kernel methods to the estimation of time-varying distributions and densities subject to moment constraints. Chapter 2 proposes a weighted kernel density estimator for a time-varying probability density function and the corresponding cumulative …
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Kernel Estimators in Complex Data Analysis
Kernel estimators, including kernel density estimators and kernel regression estimators, have drawn great research interests in terms of both theoretical studies and applications since invention, due to their easy interpretation and flexibility to model data with complicated density …
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Solar activity detection and prediction using image processing and machine learning techniques
… flare prediction, it is modeled as a conditional density estimation (CDE) problem. A novel method is proposed to solve the CDE problem using kernel-based nonlinear regression and moment-based density function reconstruction techniques. This method involves two main steps. In the first step, …
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Nonlinear dynamic process monitoring using kernel methods
The application of kernel methods in process monitoring is well established. How- ever, there is need to extend existing techniques using novel implementation strate- gies in order to improve process monitoring performance. For example, process monitoring using kernel principal component analysis …
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Novel methods of supernova classification and type probability estimation
… from their contaminating counterparts, kernel density estimation and boosting. In the second half of this thesis we shift focus from classification to the related problem of type probability estimation, and ask how best to use type probabilities.
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Graphical tools for the examination of high-dimensional functions obtained as the result of Bayesian analysis
… – especially Projection Pursuit methods – and Kernel Density Estimation (KDE). During the course of this work acceptable prior technology was found for MCMC and, once the decision for it had been made, Projection Pursuit. However, the current state of KDE gave rise to several objections. Not …
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Smooth Interactive Visualization
… histograms are a non-smooth visualization of density. Not only are histograms non-smooth visually, but they are also non-smooth over their parameter space, as they change abruptly in response to smooth change of bin width or bin offset. For large data visualization, histograms are commonly …
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Practical methods for data mining with massive data sets
… data sets. Advanced techniques such as binned kernel density estimation, tours, and mode-based projection pursuit will be explored. Mean-centered binning will be introduced as an improved method for binned density estimation. The density grand tour will be demonstrated as a means of exploring …
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Four Essays on Managerial Incentives
… of the entire distributions of returns using kernel density estimators rejects the ""news is no longer newsworthy"" idea."
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Using information theoretic measures to evaluate support vector machine kernels
… in the input data to evaluate the ability of a kernel to successfully separate a class in some feature space. This method is built on the fundamental idea that kernel density estimation in some input space is equivalent to an inner product on some Hilbert space. Estimators of Renyi's generalized …
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