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Showing 1 to 12 of 12 for “"KERNEL 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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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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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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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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Estimation of a density function with applications to reliability
… application to reliability analysis. The estimator of the unknown density function developed in Chapter II is similar to one proposed by Rosenblatt (1956) and by Parzen (1962). However, the kernel we consider is a function of the rank of each observation. We use, as our kernel, the …
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On robust corner-preserving smoothing in image processing
… and dicontinuity-preserving issues of M-kernel estimators in one- and two-dimensional regression. The M-kernel smoother was first introduced by Härdle and Gasser (1984) who provide a restricted valid proof of consistency for a monotone score function and show the minimax property. Chu et …
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Three Essays in Applied Econometrics: with Application to Natural Resource and Energy Markets
… data, such as the nonparametric local linear kernel estimator, the Nadraya-Watson estimator, and the k-Nearest Neighbors method developed by Hallin et al. (2004b), Lu and Chen (2002), P.M. Robinson (2011) and Li and Tran (2009). With data sampled on a rectangular grid in a nonlinear random …
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Methods for Quantitatively Describing Tree Crown Profiles of Loblolly pine (<I>Pinus taeda</I> L.)
… in the curve. A class of local-polynomial estimators which contains the popular kernel estimator as a special case was investigated. Kernel regression appears to fit closely to the interior data points but often possesses bias problems at the boundaries of the data, a feature less exhibited …
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The Foraging Ecology, Habitat Use, and Population Dynamics of the Laysan teal (Anas laysanensis)
… range size was 9.78 ha (SE 2.6) using the fixed kernel estimator (95% kernel; 15 birds with >25 locations). Foraging was strongly influenced by time of day: birds spent only 4% of their time foraging in the day, but spent 45% of their time foraging at night. Time activity budgets from the …
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Efficient nonparametric inference for discretely observed compound Poisson processes
… divisible distributions, we construct new estimators of the jump distribution and of the so-called Lévy distribution. Under mild assumptions, we prove Donsker theorems for both (i.e. functional central limit theorems with the uniform norm) and identify the limiting Gaussian processes. This …
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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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ΑΣΘΕΝΩΣ ΕΞΗΡΤΗΜΕΝΕΣ ΤΥΧΑΙΕΣ ΜΕΤΑΒΛΗΤΕΣ ΚΑΙ ΜΗ ΠΑΡΑΜΕΤΡΙΚΗ ΕΚΤΙΜΗΣΗ ΠΥΚΝΟΤΗΤΑΣ ΠΙΘΑΝΟΤΗΤΑΣ
… STRONG UNIFORMLY CONSISTENCY OF NONPARAMETRIC ESTIMATORS ONTHE COMPACT SUBSETS OF IR+, T=1,2,..., AND THE WHOLE SPACE.