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Articles 1 - 7 of 7
Full-Text Articles in Applied Statistics
Analysis Of Ranked Gene Tree Probability Distributions Under The Coalescent Process For Detecting Anomaly Zones, Anastasiia Kim
Analysis Of Ranked Gene Tree Probability Distributions Under The Coalescent Process For Detecting Anomaly Zones, Anastasiia Kim
Shared Knowledge Conference
In phylogenetic studies, gene trees are used to reconstruct species tree. Under the multispecies coalescent model, gene trees topologies may differ from that of species trees. The incorrect gene tree topology (one that does not match the species tree) that is more probable than the correct one is termed anomalous gene tree (AGT). Species trees that can generate such AGTs are said to be in the anomaly zone (AZ). In this region, the method of choosing the most common gene tree as the estimate of the species tree will be inconsistent and will converge to an incorrect species tree when …
Preface, Weixing Song
Understanding Sexual Violence Against Women, Maria Martinez
Understanding Sexual Violence Against Women, Maria Martinez
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Pooling Of Variances: The Skeleton In The Mixed Model Closet?, Philip M. Dixon
Pooling Of Variances: The Skeleton In The Mixed Model Closet?, Philip M. Dixon
Conference on Applied Statistics in Agriculture and Natural Resources
I explore three related issues concerning pooling of error variances: when is it appropriate (or not) to pool, how best to evaluate equality of variances, and whether there is a cost to never pooling. I focus on pooling decisions in a combined analysis of a multi-site experiment. A-priori, sites should have different error variances. My primary question is whether an analysis that ignores unequal variances is wrong.
I find that ignoring heteroscedasticity between sites maintains, or provides slightly conservative, tests of average treatment effects and treatment-by-site interactions. Models with site-specific variances do provide more powerful tests when variances are different. …
An Optimized Route For Q100'S Bert And Kristin To Visit All Jersey Mike's Subs In Atlanta For Charity
Symposium of Student Scholars
The Bert Show is a popular morning show on Atlanta’s Q100 radio station. They host a non-profit organization that provides a “magical, all-expenses-paid, five-day journey to Walt Disney World for children with chronic and terminal illnesses and their families” called “Bert’s Big Adventure.” On March 28th, 2018, thirty-seven locations of Jersey Mike’s are participating in the their Jersey Mike’s Day of Giving to support Bert’s Big Adventure. The goal is to have two popular radio show hosts visit each of these locations for some photos and presence to draw in more customers! But how do we get two …
A Comparison Of Unsupervised Methods For Dna Microarray Leukemia Data, Denise Harness
A Comparison Of Unsupervised Methods For Dna Microarray Leukemia Data, Denise Harness
Appalachian Student Research Forum
Advancements in DNA microarray data sequencing have created the need for sophisticated machine learning algorithms and feature selection methods. Probabilistic graphical models, in particular, have been used to identify whether microarrays or genes cluster together in groups of individuals having a similar diagnosis. These clusters of genes are informative, but can be misleading when every gene is used in the calculation. First feature reduction techniques are explored, however the size and nature of the data prevents traditional techniques from working efficiently. Our method is to use the partial correlations between the features to create a precision matrix and predict which …
Building A Better Risk Prevention Model, Steven Hornyak
Building A Better Risk Prevention Model, Steven Hornyak
National Youth Advocacy and Resilience Conference
This presentation chronicles the work of Houston County Schools in developing a risk prevention model built on more than ten years of longitudinal student data. In its second year of implementation, Houston At-Risk Profiles (HARP), has proven effective in identifying those students most in need of support and linking them to interventions and supports that lead to improved outcomes and significantly reduces the risk of failure.