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 26 for “"Kernel regression"”.
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Optimal bandwidth selection rule for kernel regression estimator with dependent variables
… d}$).) We consider kernel estimators of m(x). Recently, convergence properties of the kernel estimator have been developed under certain dependence structures for the process (X$\sb{\rm t}$,Y$\sb{\rm t}$). One of the crucial points in applying a kernel estimator is …
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Learning manifolds with the Parametrized Self-Organizing Map and Unsupervised Kernel Regression
… the manifold learning algorithm Unsupervised Kernel Regression (UKR) is introduced as a counterpart to the classical Nadaraya-Watson estimator. In a nutshell, UKR requires very little parameters to be chosen a priori: In its simplest form, a UKR model is fully specified by the dimensionality …
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Representation discovery in non-parametric reinforcement learning
… now practical non-parametric algorithms that use kernel regression to approximate value functions. The correctness guarantees of kernel regression require that the underlying value function be smooth. Most problems of interest do not satisfy this requirement in their native space, but can be …
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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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Neuroninių tinklų architektūros parinkimas /
… is used to construct sparse generalized Gaussian Kernel regression model- so called neural network. Kernel which maximize Renyi entropy is used too. Experimental results obtained using these models are promising.
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Bayesian Kernel Models for Statistical Genetics and Cancer Genomics
… of this thesis is to examine the utility of kernel regression ap- proaches and variance component models for solving complex problems in statistical genetics and molecular biology. Many of these types of statistical methods have been developed specifically to be applied to solve similar …
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Technical analysis : neural network based pattern recognition of technical trading indicators, statistical evaluation of their predictive value and a historical overview of the field
We revisit the kernel regression based pattern recognition algorithm designed by Lo, Mamaysky, and Wang (2000) to extract nonlinear patterns from the noisy price data, and develop an analogous neural network based one. We argue that, given the natural flexibility of neural network models and the …
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HATLINK: a link between least squares regression and nonparametric curve estimation
For both least squares and nonparametric kernel regression, prediction at a given regressor location is obtained as a weighted average of the observed responses. For least squares, the weights used in this average are a direct consequence of the form of the parametric model prescribed by the user. …
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Testing the profitability of technical analysis in Singapore and Malaysian stock markets
… recognition algorithm based on local polynomial regression to identify technical chart patterns that is an improvement over the kernel regression approach developed by Lo, Mamaysky and Wang.
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Enhanced modeling methodology for system-level electrostatic discharge simulation
… most notably the transient voltage suppressor. Kernel regression is used to generate an enhanced quasistatic I-V model of an IC pin, which reflects its dependency on the circuit board’s power delivery network. S-parameter measurements enable the development of a model for an IEC 61000-4-2 ESD …
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Beneficial Initializations in Over-Parameterized Machine Learning Problems
… by showing that in over-parameterized linear regression, transfer learning is equivalent to solving regression from a non-zero initialization. We use this finding to propose LLBoost, a theoretically grounded, computationally efficient method to boost the validation and test accuracy of …
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Kernel Machines are Not Black Boxes - On the Interpretability of Kernel-based Nonparametric Models
Kernel-based nonparametric models are often perceived as uninterpretable black boxes. This notion is however questionable; permutation-based variable importance, for example, is one approach for interpreting various nonparametric models. Unfortunately, besides being computationally intensive, …
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Latent variable model estimation via collaborative filtering
… based collaborative filtering can be viewed as kernel regression for latent variable models, where the features are not directly observed and the kernel must be estimated from the data. In addition, while classical collaborative filtering typically requires a dense dataset, this thesis proposes …
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Model-robust quantal regression
… commonly used parametric procedure is logistic regression, commonly referred to as "logit analysis." The adequacy of the fit by the logistic regression curve is tested using the chi-square lack-of-fit test. If the lack-of-fit test is not significant, then the logistic model is assumed to be …
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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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Essays on Banking and Option Pricing
… from option prices. The existing nonparametric kernel regression estimator in Ait-Sahalia and Lo (1998) does not satisfy a requirement of a probability density function: that it be non-negative on its domain. In this paper, we implement a one-step estimation and smoothing procedure based on …
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Smooth regression quantile estimation
… will be mainly focused on the local linear kernel regression quantile estimation. Different estimators within this class have been proposed, developed asymptotically and applied to real applications. I include algorithmdesign and selection of smoothing parameters. Chapter 2 studies two …
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Sequential design augmentation with model misspecification
… terms. A new methodology, based on a modified kernel regression procedure called HATLINK, is presented that incorporates model misspecification into the sequential augmentation of points in the new region. HATLINK is a combination of parametric and nonparametric regressions and is designed to …
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Methods for Quantitatively Describing Tree Crown Profiles of Loblolly pine (<I>Pinus taeda</I> L.)
… described using single-regressor, nonparametric regression analysis in an effort to improve crown representations. The resulting profiles were compared to more traditional representations. Nonparametric regression may be applicable when an underlying parametric model cannot be identified. The …
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