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Showing 1 to 14 of 14 for “"kernel density estimator"”.
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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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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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Least squares mixture decomposition estimation
The Least Squares Mixture Decomposition 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 …
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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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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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A collaborative, multi-agent based methodology for abnormal events management
… (SOM), principal components analysis (PCA), and kernel density estimator (KDE) were extended in this thesis to the domain of transient operations.
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Learning from partially labeled data
… three approaches to the problem, starting with a kernel classifier that can be interpreted as a discriminative kernel density estimator and is trained via the EM algorithm or via margin-based criteria. Secondly, we employ a Markov random walk representation that exploits clusters and …
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Spatial Ecology of the Desert Tortoise: Sampling Frequency and Biological Influences
… size, sampling frequency, and the choice of estimator. Further confounding these estimates are a number of physical, social, and ecological factors. Identifying the main determinants of space use patterns by a species may aid conservation efforts.</p> <p>The Desert Tortoise <em>(Gopherus …
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Essays on technical analysis in financial markets
… of the survey results are tested using a novel kernel density estimator based bootstrap methodology. Respondents' forecasts do bear some relationship to the size of the most recent trend by certain whole-number ratios by more often than would be expected by chance. The third experiment addresses …
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Using movement modelling to improve the design and analysis of vantage point surveys in bird and wind energy studies
… distribution of bird activity using: i) a kernel density estimator adapted to calculate the density of flight trajectories, and ii) modelling flights as being driven by a stochastic process under the influence of a potential field. Acknowledging the subjectivity introduced in the mapping of …
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Statistical risk estimation for communication system design
… Specifically, for statistics based on data, Kernel Density Estimator is selected as the preferred technique to extract probability densities from a database of previous space missions' components. Expert elicitation is generated through a four-part model which quantifies experts' sensitivity …
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Remote sensing for detecting rapid post-fire recovery as Groundwater-Dependent Ecosystems in the Cape Floristic Region
… The last level of analysis employed a kernel density estimator function to assess the recovery rate post-burn and use it to detect faster recovery as potential of wetlands to be GDEs; results showed that potential wetland GDEs experience rapid NDVI recovery > 236 days post-fire. In the …
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Regional wage disparities in post-apartheid South Africa: spatial patterns, convergence dynamics, and causes
… a range of complementary measures, namely, the kernel density estimator and indicators of α - convergence and β - convergence. These measures consistently reveal evidence of regional wage divergence between 1996 and 2001, but strong and robust evidence of regional wage convergence from 2001 to …
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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 …