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Articles 1 - 8 of 8
Full-Text Articles in Statistical Theory
Statistical Quality Control: A Bayesian Framework, Jakia Jaber Tunal
Statistical Quality Control: A Bayesian Framework, Jakia Jaber Tunal
College of Graduate Studies: Theses & Dissertations
In many industries, it is important to assess whether a machine or system is operating within acceptable limits or has gone out of control. This project applies Bayesian statistics to monitor a process over time and detect changes in its behavior. First, initial data are collected to understand the system’s typical performance and to form a starting prior distribution. As new observations arrive over time, the prior is updated through Bayesian inference, combining past information with incoming data. This iterative updating creates a continuous monitoring framework that adapts as more evidence becomes available. When the updated results suggest that the …
Accounting For Variability Due To Resampling Using Bootstrapping, Dipendra Phuyal
Accounting For Variability Due To Resampling Using Bootstrapping, Dipendra Phuyal
College of Graduate Studies: Theses & Dissertations
Bradley Efron (1979) introduced bootrapping. Typically a researcher is interested in studying a process which generates individuals. The collection of individuals the process has(actual) or could have (conceptual) generated is the population. The collection of conceptual members of the population is an uncountable collection. Hence, the population is anuncountable collection of individuals. The collection of individuals the process has generated (actual individuals) is representative of what the process can generate and will bereferred to as the representative sample. The size of this sample is a nonnegative integervalued random variable N which may be a constant random variable such as in …
The Distribution Of The Significance Level, Paul O. Monnu
The Distribution Of The Significance Level, Paul O. Monnu
College of Graduate Studies: Theses & 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
College of Graduate Studies: Theses & 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 …
Some New Generalized Distribution Via Lindley-Weibuli And Lindley-Log-Logistic Distributions With Applications, Soliu A. Raheem
Some New Generalized Distribution Via Lindley-Weibuli And Lindley-Log-Logistic Distributions With Applications, Soliu A. Raheem
College of Graduate Studies: Theses & Dissertations
In this thesis, new generalized distributions, namely Beta Lindley-Log-Logistic (BLLLoG) distribution, Marshall-Olkin Lindley-Weibull (MOLW) distribution, and Gamma LindleyWeibull (GLW) distribution as well as related sub-distributions are proposed. Series expansion of the densities are obtained. Statistical properties of these distributions, including hazard function, reverse hazard function, moments, reliability, quantile function, mean deviations, Bonferroni and Lorenz curves, entropy and Fisher information are derived. Method of maximum likelihood is used to estimate the parameters of the new distributions. Monte Carlo simulation is employed to examine the performance of the proposed distributions. Applications of the generalized distributions to real lifetime data are presented to …
Comparing K Population Means With No Assumption About The Variances, Tony Yaacoub
Comparing K Population Means With No Assumption About The Variances, Tony Yaacoub
College of Graduate Studies: Theses & 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
College of Graduate Studies: Theses & 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
College of Graduate Studies: Theses & 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.