Browsing by Title
Now showing items 76876-76895 of 121333
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(2018-06-08)This study is to compare and validate two models for random wave transformation with experimental data. Both models are based on frequency domain KdV equation. First model is a modified version of KdV equation which was ...
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(Physical Review Letters, 2010)
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(American Physical Society, 1988)
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(Texas A&M University, 2006-08-16)A general framework for performance optimization of continuous-time OTA-C (Operational Transconductance Amplifier-Capacitor) filters is proposed. Efficient procedures for evaluating nonlinear distortion and noise valid for ...
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(Texas A&M University, 2007-04-25)Conventional coupling paradigms used nowadays to couple various physics components in reactor analysis problems can be inconsistent in their treatment of the nonlinear terms. This leads to usage of smaller time steps to ...
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(2018-11-02)In this study a new computational framework by the name Graph-based Finite Element Approach (GraFEA) is developed for the study of fracture in solids. Conventional finite element method (FEM) is without doubt the most ...
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(2019-04-10)Theoretical models are constructed for various nonlocal nonlinear optical processes in graphene. Specifically, difference frequency generation of surface plasmon-polaritons in Landau quantized graphene; the generation of ...
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(Texas A&M University. Libraries, 1984)A nonparametric bivariate density estimation technique is developed employing tensor product B-splines to provide a concise wave data summary. Most of the existing nonparametric techniques involve a certain level of ...
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(Texas A&M University, 2006-10-30)Nonparametric Bayesian models have been researched extensively in the past 10 years following the work of Escobar and West (1995) on sampling schemes for Dirichlet processes. The infinite mixture representation of the ...
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(Texas A&M University. Libraries, 1974)This dissertation is concerned with nonparametric detection of a narrow-band Gaussian signal and a fixed sine wave signal in white Gaussian noise by means of the level-crossing rate of a received signa. The major contribution ...
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(2018-05-03)This dissertation includes two essays: The first one is on nonparametric inference in causal effect models, and the second one is on nonparametric estimation in financial economics. In the first essay, we propose a ...
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(2014-07-30)Estimating gradients is of crucial importance across a broad range of applied economic domains. Here we consider data-driven bandwidth selection based on the gradient of an unknown regression function. This is a difficult ...
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(Texas A&M University, 2006-10-30)In this dissertation I investigate several topics in the field of nonparametric econometrics. In chapter II, we consider the problem of estimating a nonparametric regression model with only categorical regressors. We ...
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(2013-04-23)Learning from data, especially ‘Big Data’, is becoming increasingly popular under names such as Data Mining, Data Science, Machine Learning, Statistical Learning and High Dimensional Data Analysis. In this dissertation we ...
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(2011-10-21)In this dissertation, we explore the properties of correlation structure for spatio-temporal point processes and a quantitative spatial process. Spatio-temporal point processes are often assumed to be separable; we propose ...
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(Texas A&M University. Libraries, 1974)Nonparametric analogs to Wilk's [Lambda], Pillai's V, and Hotelling's T [superscript 2, subscript 0] are proposed as multivariate discriminators. Small sample distributions for the proposed statistics are generated by a ...
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(Texas A&M University, 2004-11-15)A common requirement for spatial analysis is the modeling of the second-order structure. While the assumption of isotropy is often made for this structure, it is not always appropriate. A conventional practice to check for ...
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(Texas A&M University. Libraries, 1980)In this dissertation, an approach to representing the covariance structure of spatial random variables is presented. A number of methods for fitting polygonal functions to variograms are demonstrated. Techniques for ...
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(2019-11-20)In production economics, the cost function is a critical tool used to infer productivity and efficiency measures to describe the key features of an industry. This dissertation investigates nonparametric estimators with ...