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 14 of 14 for “"kernel estimation"”.
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Motion blur removal from photographs
… estimate how the image is blurred (i.e. the blur kernel or the point-spread function) and (ii) restore a natural looking image through deconvolution. Blur kernel estimation is challenging because the algorithm needs to distinguish the correct imageblur pair from incorrect ones that can also …
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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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Bandwidth Selection for Level Set Estimation in the Context of Regression and a Simulation Study for Non Parametric Level Set Estimation When the Density Is Log-Concave
Bandwidth selection is critical for kernel estimation because it controls the amount of smoothing for a function's estimator. Traditional methods for bandwidth selection involve optimizing a global loss function (e.g. least squares cross validation, asymptotic mean integrated squared error). …
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Essays in econometrics
… chapters with a focus on the identification and estimation of causal effects. We consider various empirical strategies that are commonly used in the field of social sciences in general, and in the field of economics in particular. The first chapter considers estimating heterogeneous causal …
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Performance Limits of Active Noise Control for Nonlinear Systems: A Machine Learning Perspective
… that is able to calculate the third-order Wiener kernel for nonlinear systems with practical memory lengths. The GPGWS is used to quantify system complexity via higher-order kernel estimation, offering new insights into CNN learnability. One perspective is that CNNs learn a Wiener-like …
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Accelerating magnetic resonance imaging by unifying sparse models and multiple receivers
… The second approach involves modifying the kernel estimation step of GRAPPA to promote sparsity in the reconstructed image and mitigate the noise amplification typically encountered with parallel imaging. The third approach involves imposing a sparsity prior on the coil images and estimating …
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Calibration of Option Pricing in Reproducing Kernel Hilbert Space
… of finding the local volatility in a reproducing kernel Hilbert space. We defined a new volatility function which allows us to embrace both the financial and time factors of the options. We discuss the existence of the minimizer by using regu- larized reproducing kernel method and show that the …
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First- and Second-Order Properties of Spatiotemporal Point Patterns in the Space-Time and Frequency Domains
… point patterns are explicitly defined. Estimation of first-order intensities are examined using 3-dimensional smoothing techniques. Conditions for weak stationarity are provided so that subsequent second-order analysis can be conducted. We consider second-order analysis of spatiotemporal …
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Improving partial mutual information based input variable selection for data driven environmental and water resources models
… two steps in the PMI process, including the estimation of MI/PMI and the estimation of the residuals. In terms of MI/PMI estimation, this requires kernel density estimates of the modelling data to be obtained for the estimation of marginal and joint probability density functions (PDFs), which …
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NONPARAMETRIC IDENTIFICATION AND ESTIMATION OF STOCHASTIC FRONTIER MODELS
… studies nonparametric identication and estimation of stochastic frontiermodels. It is composed of three chapters. The rst chapter investigates the identication and estimation of a cross sectional stochastic frontier model with Laplacian errors and unknown variance, which is built on a …
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Essays on Nonparametric Estimation of Asset Pricing Models
… of returns and in the shape of the pricing kernel. More specifically, Chapter 1 studies the use of noisy high-frequency data to estimate the time-varying state-price density implicit in European option prices. A dynamic kernel estimator of the conditional pricing function and its derivatives …
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Quantum Machine Learning Applications and Algorithms
L'abstract è presente nell'allegato / the abstract is in the attachment
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ΑΣΘΕΝΩΣ ΕΞΗΡΤΗΜΕΝΕΣ ΤΥΧΑΙΕΣ ΜΕΤΑΒΛΗΤΕΣ ΚΑΙ ΜΗ ΠΑΡΑΜΕΤΡΙΚΗ ΕΚΤΙΜΗΣΗ ΠΥΚΝΟΤΗΤΑΣ ΠΙΘΑΝΟΤΗΤΑΣ
IN THIS WORK THE DEFINITION OF WEAKLY DEPENDENT RANDOM VARIABLES ARE GIVEN. KNOWN AND NEW RESULTS ARE GIVEN WITH A VIEW TO MAKING THEM READILY AVAILABLE FOR CERTAIN STATISTICAL APPLICATIONS. EXPONENTIAL PROBABILITY BOUNDS FOR SUMS OF TRIANGULAR ARRAY OF WEAKLY DEPENDENT RANDOM VARIABLES ARE GIVEN, …