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Full-Text Articles in Statistical Methodology
Methods To Improve Inference From Dependent Network Data, Dongah Kim
Methods To Improve Inference From Dependent Network Data, Dongah Kim
Doctoral Dissertations
Over the past decade, network research has increased dramatically. Network data are used in many fields because they contain not only covariates of each observation, but also `relationships' between observations. Therefore, statistical analysis of network data has been rapidly developed. However, network data presents many challenges, such as collecting network data, inferring the prevalence of an outcome of interest, and valid statistical testing typically with highly dependent data. The methods discussed in this thesis are developed to improve statistical inference from dependent network data.
Error Costs, Legal Standards Of Proof And Statistical Significance, Michelle Burtis, Jonah B. Gelbach, Bruce H. Kobayashi
Error Costs, Legal Standards Of Proof And Statistical Significance, Michelle Burtis, Jonah B. Gelbach, Bruce H. Kobayashi
All Faculty Scholarship
The relationship between legal standards of proof and thresholds of statistical significance is a well-known and studied phenomena in the academic literature. Moreover, the distinction between the two has been recognized in law. For example, in Matrix v. Siracusano, the Court unanimously rejected the petitioner’s argument that the issue of materiality in a securities class action can be defined by the presence or absence of a statistically significant effect. However, in other contexts, thresholds based on fixed significance levels imported from academic settings continue to be used as a legal standard of proof. Our positive analysis demonstrates how a …
Statistical Inferences For Functions Of Parameters Of Several Pareto And Exponential Populations With Application In Data Traffic, Sumith Gunasekera
Statistical Inferences For Functions Of Parameters Of Several Pareto And Exponential Populations With Application In Data Traffic, Sumith Gunasekera
UNLV Theses, Dissertations, Professional Papers, and Capstones
In this dissertation, we discuss the usability and applicability of three statistical inferential frameworks--namely, the Classical Method, which is sometimes referred to as the Conventional or the Frequentist Method, based on the approximate large sample approach, the Generalized Variable Method based on the exact generalized p -value approach, and the Bayesian Method based on prior densities--for solving existing problems in the area of parametric estimation. These inference procedures are discussed through Pareto and exponential distributions that are widely used to model positive random variables relevant to social, scientific, actuarial, insurance, finance, investments, banking, and many other types of observable phenomena. …