Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- ETD (3)
- ANOVA (1)
- Bias (1)
- Bootstrap confidence interval (1)
- Copula (1)
-
- Cumulative Distribution Function (1)
- Fixed effects one-way design (1)
- Generalized Inverse Weibull Distribution (1)
- Kendall Tau (1)
- Likelihood ratio test (1)
- Mixture Model (1)
- New modified Wiebull distribution (1)
- Noncentral F-distribution (1)
- Noncentral t-distribution (1)
- Observed significance level (1)
- Orthogonal designs (1)
- P-value. (1)
- Proportional Inverse Weibull Distribution (1)
- Rank Correlation (1)
- Regression changepoints (1)
- Spearman Rho (1)
- Statistically designed experiments (1)
- Weighted distribution (1)
Articles 1 - 5 of 5
Full-Text Articles in Statistics and Probability
The Distribution Of The Significance Level, Paul O. Monnu
The Distribution Of The Significance Level, Paul O. Monnu
Electronic Theses and Dissertations
Reporting the p-value is customary when conducting a test of hypothesis or significance. The likelihood of getting a fictitious second sample and presuming the null hypothesis is correct is the p-value. The significance level is a statistic that interests us to investigate. Being a statistic, it has a distribution. For the F-test in a one-way ANOVA and the t-tests for population means, we define the significance level, its observed value, and the observed significance level. It is possible to derive the significance level distribution. The t-test and the F-test are not without controversy. Specifically, we demonstrate that as sample size …
Bias Of Rank Correlation Under A Mixture Model, Russell Land
Bias Of Rank Correlation Under A Mixture Model, Russell Land
Electronic Theses and Dissertations
This thesis project will analyze the bias in mixture models when contaminated data is present. Specifically, we will analyze the relationship between the bias and the mixing proportion, p, for the rank correlation methods Spearman’s Rho and Kendall’s Tau. We will first look at the history of the two non-parametric rank correlation methods and the sample and population definitions will be introduced. Copulas will be introduced to show a few ways we can define these correlation methods. After that, mixture models will be defined and the main theorem will be stated and proved. As an example, we will apply this …
Comparing K Population Means With No Assumption About The Variances, Tony Yaacoub
Comparing K Population Means With No Assumption About The Variances, Tony Yaacoub
Electronic Theses and Dissertations
In the analysis of most statistically designed experiments, it is common to assume equal variances along with the assumptions that the sample measurements are independent and normally distributed. Under these three assumptions, a likelihood ratio test is used to test for the difference in population means. Typically, the assumption of independence can be justified based on the sampling method used by the researcher. The likelihood ratio test is robust to the assumption of normality. However, the equality of variances is often difficult to justify. It has been found that the assumption of equal variances cannot be made even after transforming …
Generalized Weibull And Inverse Weibull Distributions With Applications, Valeriia Sherina
Generalized Weibull And Inverse Weibull Distributions With Applications, Valeriia Sherina
Electronic Theses and Dissertations
In this thesis, new classes of Weibull and inverse Weibull distributions including the generalized new modified Weibull (GNMW), gamma-generalized inverse Weibull (GGIW), the weighted proportional inverse Weibull (WPIW) and inverse new modified Weibull (INMW) distributions are introduced. The GNMW contains several sub-models including the new modified Weibull (NMW), generalized modified Weibull (GMW), modified Weibull (MW), Weibull (W) and exponential (E) distributions, just to mention a few. The class of WPIW distributions contains several models such as: length-biased, hazard and reverse hazard proportional inverse Weibull, proportional inverse Weibull, inverse Weibull, inverse exponential, inverse Rayleigh, and Frechet distributions as special cases. Included …
Finding A Better Confidence Interval For A Single Regression Changepoint Using Different Bootstrap Confidence Interval Procedures, Bodhipaksha Thilakarathne
Finding A Better Confidence Interval For A Single Regression Changepoint Using Different Bootstrap Confidence Interval Procedures, Bodhipaksha Thilakarathne
Electronic Theses and Dissertations
Recently a number of papers have been published in the area of regression changepoints but there is not much literature concerning confidence intervals for regression changepoints. The purpose of this paper is to find a better bootstrap confidence interval for a single regression changepoint. ("Better" confidence interval means having a minimum length and coverage probability which is close to a chosen significance level). Several methods will be used to find bootstrap confidence intervals. Among those methods a better confidence interval will be presented.