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Session 13: On Statistical Estimates Of The Inverted Kumaraswamy Distribution Under Adaptive Type-I Progressive Hybrid Censoring, Qingqing Li, Yuhlong Lio
Session 13: On Statistical Estimates Of The Inverted Kumaraswamy Distribution Under Adaptive Type-I Progressive Hybrid Censoring, Qingqing Li, Yuhlong Lio
SDSU Data Science Symposium
The probability distribution modeling is investigated via maximum likelihood estimation method based on adaptive type-I progressively hybrid censored samples from the inverted Kumaraswamy distribution. The point estimates of model parameters, reliability, hazard rate and quantile are obtained and confidence intervals are also developed by using asymptotic distribution as well as bootstrap method. Monte Carlo simulation has been performed to evaluate the accuracy of estimations. Finally, a real data set is given for the application illustration.
An Alpha-Based Prescreening Methodology For A Common But Unknown Source Likelihood Ratio With Different Subpopulation Structures, Dylan Borchert, Semhar Michael, Christopher Saunders, Andrew Simpson
An Alpha-Based Prescreening Methodology For A Common But Unknown Source Likelihood Ratio With Different Subpopulation Structures, Dylan Borchert, Semhar Michael, Christopher Saunders, Andrew Simpson
SDSU Data Science Symposium
Prescreening is a commonly used methodology in which the forensic examiner includes sources from the background population that meet a certain degree of similarity to the given piece of evidence. The goal of prescreening is to find the sources closest to the given piece of evidence in an alternative source population for further analysis. This paper discusses the behavior of an $\alpha-$based prescreening methodology in the form of a Hotelling $T^2$ test on the background population for a common but unknown source likelihood ratio. An extensive simulation study with synthetic and real data were conducted. We find that prescreening helps …
Identifying Subpopulations Of A Hierarchical Structured Data Using A Semi-Supervised Mixture Modeling Approach, Andrew Simpson, Semhar Michael, Christopher Saunders, Dylan Borchert
Identifying Subpopulations Of A Hierarchical Structured Data Using A Semi-Supervised Mixture Modeling Approach, Andrew Simpson, Semhar Michael, Christopher Saunders, Dylan Borchert
SDSU Data Science Symposium
The field of forensic statistics offers a unique hierarchical data structure in which a population is composed of several subpopulations of sources and a sample is collected from each source. This subpopulation structure creates a hierarchical layer. We propose using a semi-supervised mixture modeling approach to model the subpopulation structure which leverages the fact that we know the collection of samples came from the same, yet unknown, source. A simulation study based on a famous glass data was conducted and shows this method performs better than other unsupervised approaches which have been previously used in practice.
Session 5: Equipment Finance Credit Risk Modeling - A Case Study In Creative Model Development & Nimble Data Engineering, Edward Krueger, Landon Thompson, Josh Moore
Session 5: Equipment Finance Credit Risk Modeling - A Case Study In Creative Model Development & Nimble Data Engineering, Edward Krueger, Landon Thompson, Josh Moore
SDSU Data Science Symposium
This presentation will focus first on providing an overview of Channel and the Risk Analytics team that performed this case study. Given that context, we’ll then dive into our approach for building the modeling development data set, techniques and tools used to develop and implement the model into a production environment, and some of the challenges faced upon launch. Then, the presentation will pivot to the data engineering pipeline. During this portion, we will explore the application process and what happens to the data we collect. This will include how we extract & store the data along with how it …