At a time when historical biogeography appears to be again expanding its scope after a period of focusing primarily on discerning area relationships using cladograms, new inference methods are needed to bring more kinds of data to bear on questions about the geographic history of lineages. Here we describe a likelihood framework for inferring the evolution of geographic range on phylogenies that models lineage dispersal and local extinction in a set of discrete areas as stochastic events in continuous time. Unlike existing methods for estimating ancestral areas, such as dispersal-vicariance analysis, this approach incorporates information on the timing of both lineage divergences and the availability of connections between areas (dispersal routes). Monte Carlo methods are used to estimate branch-specific transition probabilities for geographic ranges, enabling the likelihood of the data (observed species distributions) to be evaluated for a given phylogeny and parameterized paleogeographic model. We demonstrate how the method can be used to address two biogeographic questions: What were the ancestral geographic ranges on a phylogenetic tree? How were those ancestral ranges affected by speciation and inherited by the daughter lineages at cladogenesis events? For illustration we use hypothetical examples and an analysis of a Northern Hemisphere plant clade (Cercis), comparing and contrasting inferences to those obtained from dispersal-vicariance analysis. Although the particular model we implement is somewhat simplistic, the framework itself is flexible and could readily be modified to incorporate additional sources of information and also be extended to address other aspects of historical biogeography.
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Surv Methodol
December 2024
Department of Statistical Science, 214a Old Chemistry Building, Duke University, Durham, NC 27708-0251.
When seeking to release public use files for confidential data, statistical agencies can generate fully synthetic data. We propose an approach for making fully synthetic data from surveys collected with complex sampling designs. Our approach adheres to the general strategy proposed by Rubin (1993).
View Article and Find Full Text PDFWe introduce a computational topology-based approach with unsupervised machine-learning algorithms to estimate the database size and content of RNA-like graph topologies. Specifically, we apply graph theory enumeration to generate all 110,667 possible 2D dual graphs for vertex numbers ranging from 2 to 9. Among them, only 0.
View Article and Find Full Text PDFBiostatistics
December 2024
Department of Statistics, University of Connecticut, 215 Glenbrook Road Unit 4120, Storrs, CT 06269, United States.
Patients with type 2 diabetes need to closely monitor blood sugar levels as their routine diabetes self-management. Although many treatment agents aim to tightly control blood sugar, hypoglycemia often stands as an adverse event. In practice, patients can observe hypoglycemic events more easily than hyperglycemic events due to the perception of neurogenic symptoms.
View Article and Find Full Text PDFStat Med
February 2025
Biostatistics, Innovatio Statistics Inc., Bridgewater, New Jersey, USA.
Sample size re-estimation (SSR) is perhaps the most used adaptive procedure in both frequentist and Bayesian adaptive designs for clinical trials. The primary focus of all current frequentist and Bayesian SSR procedures is type I error control. We propose a hybrid frequentist-Bayesian SSR approach that focuses on optimizing operating characteristics (OC), which uses simulations to investigate the associated OC and adjusts accordingly.
View Article and Find Full Text PDFSci Rep
January 2025
CALCE University of Maryland, College Park, MD, 20742, USA.
Remaining useful life (RUL) prediction is a crucial aspect of the prognostics health management of lithium-ion batteries (LIBs). Owing to the influence of resampling technology, particle degradation is often observed in the particle filter-based RUL prediction of LIBs, resulting in a low prediction accuracy and large uncertainty. In this paper, a novel particle flow filter with the grey model method (GM-PFF) is proposed to forecast the RUL and state of health of batteries.
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