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Articles 1 - 5 of 5
Full-Text Articles in Statistics and Probability
Power And Sample Size Estimation For Nonparametric Composite Endpoints: Practical Implementation Using Data Simulations, Paul M. Brown, Justin A. Ezekowitz
Power And Sample Size Estimation For Nonparametric Composite Endpoints: Practical Implementation Using Data Simulations, Paul M. Brown, Justin A. Ezekowitz
Journal of Modern Applied Statistical Methods
Composite endpoints are a popular outcome in controlled studies. However, the required sample size is not easily obtained due to the assortment of outcomes, correlations between them and the way in which the composite is constructed. Data simulations are required. A macro is developed that enables sample size and power estimation.
Examination And Comparison Of The Performance Of Common Non-Parametric And Robust Regression Models, Gregory F. Malek
Examination And Comparison Of The Performance Of Common Non-Parametric And Robust Regression Models, Gregory F. Malek
Electronic Theses and Dissertations
ABSTRACT
Examination and Comparison of the Performance of Common Non-Parametric and Robust Regression Models
By
Gregory Frank Malek
Stephen F. Austin State University, Masters in Statistics Program,
Nacogdoches, Texas, U.S.A.
This work investigated common alternatives to the least-squares regression method in the presence of non-normally distributed errors. An initial literature review identified a variety of alternative methods, including Theil Regression, Wilcoxon Regression, Iteratively Re-Weighted Least Squares, Bounded-Influence Regression, and Bootstrapping methods. These methods were evaluated using a simple simulated example data set, as well as various real data sets, including math proficiency data, Belgian telephone call data, and faculty …
Tree-Based Regression For Interval-Valued Data, Chih-Ching Yeh
Tree-Based Regression For Interval-Valued Data, Chih-Ching Yeh
All Graduate Plan B and other Reports, Spring 1920 to Spring 2023
Regression methods for interval-valued data have been increasingly studied in recent years. As most of the existing works focus on linear models, it is important to note that many problems in practice are nonlinear in nature and therefore development of nonlinear regression tools for intervalvalued data is crucial. In this project, we propose a tree-based regression method for interval-valued data, which is well applicable to both linear and nonlinear problems. Unlike linear regression models that usually require additional constraints to ensure positivity of the predicted interval length, the proposed method estimates the regression function in a nonparametric way, so the …
Outlier Impact And Accommodation On Power, Hongjing Liao, Yanju Li, Gordon P. Brooks
Outlier Impact And Accommodation On Power, Hongjing Liao, Yanju Li, Gordon P. Brooks
Journal of Modern Applied Statistical Methods
The outliers’ influence on power rates in ANOVA and Welch tests at various conditions was examined and compared with the effectiveness of nonparametric methods and Winsorizing in minimizing the impact of outliers. Results showed that, considering both power and Type I error, a nonparametric test is the safest choice to control the inflation of Type I error with a decent sample size and yield relatively high power.
The Nonparametric Estimation Of Elliptical Distributions, Panfeng Liang
The Nonparametric Estimation Of Elliptical Distributions, Panfeng Liang
Open Access Theses & Dissertations
In practice, many multivariate datasets have identical marginal distributions. Elliptical distributions can be used to model many of those datasets. In this Thesis, we will propose a Bayesian method using Markov chain Monte Carlo (MCMC) methods to estimate the density function underlying multivariate datasets assuming it is an elliptical distribution.