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 18 of 18 for “"Smoothing Splines"”.
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Bayesian Inference in Nonparametric Logistic Regression
… estimated nonparametrically, using generalized smoothing splines.
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Cure Rate Models with Nonparametric Form of Covariate Effects
… is of practical interest. Here we propose two smoothing-splines based models falling respectively into the popular classes of two component mixture cure rate models and promotion time cure rate models. Under the framework of two component mixture cure rate model, Wang, Du and Liang (2012) have …
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Nonparametric function smoothing: fiducial inference of free knot splines and ecological applications
… such as locally weighted regression and smoothing splines are being used in a variety of circumstances to address questions at the forefront of ecological theory. We first examine an ecological threshold problem and define a threshold as where the derivative of the estimated functions …
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Recursive methods for statistical prediction with applications
… is introduced to compute the generalized smoothing spline estimate, and it is especially useful in calculating the leave-one-out estimates. Two cross validation functions (Kullback-Leibler and least squares cross validation) for estimating the smoothing parameter in the generalized …
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Mathematical Methods for the Approximation of Radar Traces
… times. Those data typically requires extensive smoothing and corrections before it can be used for later analysis. Conventional smoothing approaches fail to model the movement physically correct, i.e. do not take standstills of aircrafts into account. In this thesis we develop a method to detect …
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Cure Rate Model with Spline Estimated Components
… We introduce a non-parametric model employing smoothing splines. It provides non-parametric smooth estimates for both hazard function and cure rate. By introducing a latent cure status variable, we implement the method using a smooth EM algorithm. Louis' formula for covariance estimation in an …
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High performance digital volume correlation
… of spline interpolation, and using smoothing splines to address noisy image data. For DVC, the volume of data, number of correlation points, and work to solve each correlation point all grow cubically. We therefore employ parallel computing to handle this tremendous increase in …
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Essays on Banking and Option Pricing
… paper, we implement a one-step estimation and smoothing procedure based on constrained quantile smoothing splines. It is inherently robust to extreme observations and the structure of the minimization problem makes it quite simple to add shape restrictions. Monte Carlo simulations show that …
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Statistical Methods For Assessing Structural Change In Human & Microbial Genomes
… employs nonlinear statistical models of cubic smoothing splines and robust smoothing splines to analyze the association between the number of shadow counts and the number of error-free read counts. Traditional approaches to simulation when analyzing sequencing data may not reflect the real …
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Essays in asset prices and macroeconomics
… here to obtain the RNDs from option price data, smoothing splines. An application for the Brazilian exchange rate is implemented to show the usefulness of this methodology, especially to identify changes on market expectations for emerging market's exchange rates in the wake of the US ""taper …
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On Numerical Error Estimation for the Finite-Volume Method with an Application to Computational Fluid Dynamics
… reconstruction method based on polyharmonic smoothing splines is developed to smoothly reconstruct the discrete solution and improve the accuracy of error estimates. Furthermore, a method for iteratively improving discretization error estimates is devised. Efficiency and robustness …
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Variance Change Point Detection under A Smoothly-changing Mean Trend with Application to Liver Procurement
… components, the mean function is estimated by smoothing splines as the minimizer of the penalized weighted least squares. Given the mean function, we propose a likelihood ratio test statistic for identifying the variance change point. The null distribution of the test statistic is derived …
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Statistical estimation and changepoint detection methods in public health surveillance
… temperature) or unmeasured (e.g., time-based smoothing splines or Fourier terms). An advantage of modeling background seasonality via time splines is that the amount of seasonal curvature can be controlled by the number of degrees of freedom specified for the spline. A comparison of the …
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A comparison of some methods of modeling baseline hazard function in discrete survival models
… of penalised regression spline methods in smoothing the baseline hazard function. Root mean squared error (RMSE) analysis suggests that generally all the smoothing methods performed better than the model with a discrete baseline hazard function. No single smoothing method outperformed the …
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Analysis Using Smoothing Via Penalized Splines as Implemented in LME() in R
… models. Following a review of literature on splines and mixed models, details for implementing mixed model splines are presented. The examples use an experiment in the health sciences to demonstrate how to use mixed models to generate the smoothers. The first example takes a simple one-group …
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Validation of Criteria Used to Predict Warfarin Dosing Decisions
… agreement. Generalized additive models with smoothing spline estimates were calculated for each of the 14 criteria and compared to the smoothing spline estimate for the method using actual physician decisions (considered the "gold standard"). The area between the criteria method spline and …
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Fast and stable smoothing spline analysis of variance models for large samples with applications to electroencephalography data analysis
… parameterization and algorithm used to fit a smoothing spline analysis of variance (SSANOVA) model are computationally expensive, making a generalized additive model (GAM) the preferred method for multivariate smoothing. In this thesis, I propose various approximations and algorithms to …
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Some Advanced Model Selection Topics for Nonparametric/Semiparametric Models with High-Dimensional Data
… (NGK) to recover sparsity of input variables in smoothing functions. We model the smoothing function by a least squares kernel machine and construct a nonnegative garrote on the kernel model as the function of the similarity matrix. An efficient coordinate descent/backfitting algorithm is …