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.
Results
Showing 1 to 20 of 108 for “"parametric models"”.
-
Early Vision Optimization: Parametric Models, Parallelization and Curvature
… problems of early vision. The first part studies parametric problems where model parameters are estimated in addition to an image segmentation. For a small number of parameters these problems can still be solved optimally. In the second part the focus is shifted toward curvature regularization, …
-
Comparison of linear parametric models for predicting fMRI response
In this study, five different linear parametric models including Autoregressive model (ARX), Autoregressive Moving Average Model (ARMAX), Box-Jenkins Model (BJ), Instrument Variable Model (IV) and Prediction Error Model (PEM) were used to predict the fMRI response and their performances compared. …
-
Comparison of parametric models using right censored data for breast cancer patients
… censoring survival data by using three different parametric models; exponential model, Weibull model, and log-logistic model. Data of breast cancer patients from general hospital in Johor Bahru were used to illustrate the right censoring data. When analyzing the breast cancer data, all three …
-
Semi-parametric models for response times and response accuracy in computerized testing
… to analyzing the responses themselves. Current models for response times,however, mainly focus on parametric models that have the advantage of conciseness, but may suffer from a reduced flexibility to fit real data. This thesis presents two types of semi-parametric models that combine the …
-
Compact parametric models for efficient sequential decision making in high-dimensional, uncertain domains
… when the agent knows the dynamics and reward models, but only receives information about its state through its potentially noisy sensors. One of the key challenges in the sequential decision making field is the tradeoff between optimality and tractability. To handle high-dimensional (many …
-
Analysis of Creep Behavior and Parametric Models for 2124 Al and 2124+SiC Composite
… and compared to predicted values based on a parametric approach for creep analysis. The results demonstrate the applicability of traditional creep analysis on non-traditional materials.
-
Frequentist-Bayesian Hybrid Tests in Semi-parametric and Non-parametric Models with Low/High-Dimensional Covariate
… test for the significant differences between non-parametric functions and the second one is to design a test allowing any departure of predictors of high dimensional X from constant. The implementation is also given in construction of the proposal test statistics for both problems. For the first …
-
EVALUATION, DEVELOPMENT, AND EXTENSION OF FLEXIBLE PARAMETRIC MODELS FOR THE DISTRIBUTIONAL ANALYSIS OF CENSORED AND NON-CENSORED DATA: A FRAMEWORK FOR MODELING AND COMMUNICATING THE FULL DISTRIBUTION OF HEALTH OUTCOMES.
… aims to evaluate, develop, and extend flexible parametric models to improve the estimation and communication of causal effects in survival analysis. It specifically targets the robust estimation of cumulative quantities (e.g., Risk Difference, Relative Risk) and the distributional analysis of …
-
Modeling spatial covariance functions
… performance of spatial prediction. Various parametric models have been developed to accommodate the idiosyncratic features of a given dataset. However, the parametric models may impose unjustified restrictions to the covariance structure and the procedure of choosing a specific model is …
-
Linear Mixed Model Robust Regression
Mixed models are powerful tools for the analysis of clustered data and many extensions of the classical linear mixed model with normally distributed response have been established. As with all parametric models, correctness of the assumed model is critical for the validity of the ensuing inference. …
-
Nuisance Parameter Estimation in Survival Models
… means. In particular, we illustrate that a non-parametric estimator of the conditional survival function improves the estimation of censored quantile regression when the semi-parametric assumptions of current methods are badly violated. Unfortunately, the non-parametric estimator is inefficient …
-
Evaluating Statistical Models for Baseline Characterization and Measuring Change in Environmental Monitoring Data
In Before-After monitoring studies, statistical models are used to characterize baseline (i.e., pre-disturbance) conditions, and to detect, quantify, and forecast change during operational monitoring (i.e., post-disturbance). To establish best practices for analyzing monitoring data, a model …
-
Topics In Differentially Private Statistical Inference
… and linear regression, estimation in general parametric models, and non-parametric function estimation. The increasing difficulty and generality of this series is matched by the development of differentially private algorithms such as noisy iterative hard thresholding, and of minimax lower …
-
Analysis Using Smoothing Via Penalized Splines as Implemented in LME() in R
… provide a familiar framework for estimating semi-parametric and non-parametric 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 …
-
Modern Methods in Semiparametric Statistics
The ethos that “all models are wrong but some are useful” drives many modern statistical advancements, with methods that are lean with regards to modeling assumptions often out-competing their classical counterparts when deployed on modern datasets. Such principles underpin the success of many …
-
Semiparametric Techniques for Response Surface Methodology
… is commonly utilized to model the data; however, parametric models are not always adequate. In these situations, any degree of model misspecification may result in serious bias of the estimated response. Nonparametric methods have been suggested as an alternative as they can capture structure in …
-
Nonparametric Statistical Approaches for Benchmark Dose Estimation in Quantitative Risk Assessment
… risk are conducted.First introduced are eight parametric models. The advantage of parametric models is they can produce consistent result when the selected model fits the dose-response curve very well. The simplicity of knowing the expression of these models allows for the construction of a …
-
Analysis of parametric model signal processing techniques for signature analysis
Five parametric modeling techniques have been identified to be possible alternatives to the Fast Fourier Transform (FFT) for signature analyses involving short data records. The developments in signal processing that have lead to these techniques are reviewed. Mathematical definitions for …
-
Parametric and nonparametric approaches to explain and predict nonlinear population dynamics in changing environments
… on population dynamics are studied using parametric models under equilibrium assumptions. In this context, firstly we have shown that, while the approach was originally introduced to investigate the structural stability of the classic Lotka-Volterra dynamics; it can be applied to a much …
Page 1 of 6