Global ETD Search
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Showing 1 to 10 of 10 for “"nonparametric kernel"”.
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Bayesian Variable Selection and Inference for Nonparametric Kernel Machine and Functional Models
… is developed under a generalized fused multi-kernel machine regression. This method can apply to continuous/binary/ordered categorical response variables. We demonstrate the advantage of our method using bio-photonics Raman spectroscopy to identify which molecular fingerprinting wavenumber is …
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The Generalised Gaussian Process Convolution Model
… The GGPCM provides a theoretical framework for nonparametric kernel models of multidimensional signals defined on multidimensional input spaces. We show that the GGPCM generalises and connects existing work; most notably, we derive a dual formulation of the cross-spectral mixture kernel …
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Inference of high-dimensional linear models with time-varying coefficients
… method is based on a novel combination of the nonparametric kernel smoothing technique and a Lasso bias-corrected ridge regression estimator using a bias-variance decomposition to address non-stationarity in the model. A hypothesis testing setup with familywise error control is presented …
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Essays on Banking and Option Pricing
… of the model. The third paper introduces a nonparametric procedure to estimate state-price densities 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 …
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High-dimensional Multimodal Bayesian Learning
… lower-level variables, we propose a multi-level nonparametric kernel machine approach, utilizing variational inference to jointly identify multi-level variables as well as build the network. Chapter 3 addresses the development of a simultaneous selection of functional domain subsets, selection of …
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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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Parametric, Non-Parametric And Statistical Modeling Of Stony Coral Reef Data
… index used in previous studies, using the xi nonparametric kernel density estimate method. This nonparametric procedure gives very effective estimates of the statistical measures for the jackknifing pseudovalues. Lastly, the present study develops a predictive statistical model for stony coral …
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The effects of 2007-2008 crisis on the CDS and the interbank markets: Empirical investigations
… density of interbank funding rates using nonparametric kernel methods. Second, it analyzes the effect of banks size, the operating currency and banks' nationality on the cross-sectional distribution of these rates. The findings strongly support the statistical significance of these effects …
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Supervised Machine Learning Under Test-Time Resource Constraints: A Trade-off Between Accuracy and Cost
… which could be substantial, in particular nonparametric algorithms. We then propose three strategies to explicitly trade-off accuracy and the two components of test-time cost during classifier training.</p><p>To budget the feature extraction cost, we first introduce two algorithms: …
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Essays in capital markets
… approach to technical pattern recognition using nonparametric kernel regression, and apply this method to a large number of U.S. stocks from 1962 to 1996 to evaluate the effectiveness of technical analysis. By comparing the unconditional empirical distribution of daily stock returns to the …