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- Keyword
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- Additive Manufacturing (1)
- Bayesian inference (1)
- Carnivore community (1)
- Design-based inference (1)
- Differential recruitment (1)
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- Kasanka National Park Zambia and Banni Grasslands Kutch India (1)
- Macroecology and community ecology (1)
- Mammal monitoring and conservation (1)
- Metamodel (1)
- Misclassification on nodal attribute (1)
- Multispecies occupancy models (1)
- National prevalence estimation (1)
- Predictive analytic (1)
- Respondent-driven sampling (1)
- Smart Manufacturing (1)
- Spatiotemporal partitioning and species interactions (1)
Articles 1 - 3 of 3
Full-Text Articles in Design of Experiments and Sample Surveys
Monitoring Mammals At Multiple Scales: Case Studies From Carnivore Communities, Kadambari Devarajan
Monitoring Mammals At Multiple Scales: Case Studies From Carnivore Communities, Kadambari Devarajan
Doctoral Dissertations
Carnivores are distributed widely and threatened by habitat loss, poaching, climate change, and disease. They are considered integral to ecosystem function through their direct and indirect interactions with species at different trophic levels. Given the importance of carnivores, it is of high conservation priority to understand the processes driving carnivore assemblages in different systems. It is thus essential to determine the abiotic and biotic drivers of carnivore community composition at different spatial scales and address the following questions: (i) What factors influence carnivore community composition and diversity? (ii) How do the factors influencing carnivore communities vary across spatial and temporal …
Model-Based Predictive Analytics For Additive And Smart Manufacturing, Zhuo Yang
Model-Based Predictive Analytics For Additive And Smart Manufacturing, Zhuo Yang
Doctoral Dissertations
Qualification and certification for additive and smart manufacturing systems can be uncertain and very costly. Using available historical data can mitigate some costs of producing and testing sample parts. However, use of such data lacks the flexibility to represent specific new problems which decreases predictive accuracy and efficiency. To address these compelling needs, in this dissertation modeling techniques are introduced that can proactively estimate results expected from additive and smart manufacturing processes swiftly and with practical levels of accuracy and reliability. More specifically, this research addresses the current challenges and limitations posed by use of available data and the high …
Inference From Network Data In Hard-To-Reach Populations, Isabelle Beaudry
Inference From Network Data In Hard-To-Reach Populations, Isabelle Beaudry
Doctoral Dissertations
The objective of this thesis is to develop methods to make inference about the prevalence of an outcome of interest in hard-to-reach populations. The proposed methods address issues specific to the survey strategies employed to access those populations. One of the common sampling methodology used in this context is respondent-driven sampling (RDS). Under RDS, the network connecting members of the target population is used to uncover the hidden members. Specialized techniques are then used to make inference from the data collected in this fashion. Our first objective is to correct traditional RDS prevalence estimators and their associated uncertainty estimators for …