A Mixture Model for Rare and Clustered Populations Under Adaptive Cluster Sampling

Gonçalves, Kelly C. M.; Moura, Fernando A. S.

doi:10.1214/15-ba961

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…The Estimation of whole number of rare events in a sparse and clustered population was considered. The model proposed gave alternative approach to the existing model of Goncalves and Moura (2016) whose assumption of units which belong to different networks was heterogeneity across the networks.…”

Section: Discussionmentioning

confidence: 99%

“…Having β to control the conditional expected number of the non-empty networks, β ∼ beta (a β , b β ), Goncalves and Moura (2016) as…”

Section: Generating Heterogeneity Among Units Within the Networkmentioning

confidence: 99%

“…Rapley and Welsh (2008) modelled adaptive cluster sample data and developed a model-based Bayesian analysis using the prior knowledge about the population to update the sampling design and make inference with homogeneity assumption among the networks. Goncalves and Moura (2016) proposed for clustered and sparse populations a unit-level mixture model when the data are obtained from an ACS considering heterogeneity among units that belong to different clusters to estimate the population total. Thompson (2017) mentioned that Adaptive link-tracing network sampling methods are significant for sampling hard to reach populations.…”

Section: Introductionmentioning

confidence: 99%

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Modelling Rare Events in an Adaptive Cluster Sampling Design with Heterogeneity among Networks and within the Network Units

Wale-Orojo

Olayiwola

Apantaku

et al. 2022

Sri Lankan J App Stats

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Rare events population (φ) is hard-to-reach, sparsely distributed and clus-tered; an Adaptive Cluster Sampling (ACS) is the design to collect information from φ. Researchers and Policy Makers have modelled φ in ACS design with homogeneity assumptions. This study modelled φ with heterogeneity among networks and within the network units. Data from the International Institute of Tropical Agriculture on Culcasia Scandens, an understory plant and simulation were used to validate the model. Estimators for total and average number of rare events were derived and their statistical properties were examined. Bayesian Model was embedded in the designed ACS to develop the model for predicting the total number of rare events. Parameters α, β and λ were used in the model to control the expected number of grid cells with rare events, the conditional expected number of sub-network and expected number of rare events in each sub-network respectively. Markov Chain Monte-Carlo Algorithm with R and Winbugs software were used to estimated these parameters. The robustness of the model was examined and its Sensitivity Analysis was

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Section: Discussionmentioning

confidence: 99%

“…Having β to control the conditional expected number of the non-empty networks, β ∼ beta (a β , b β ), Goncalves and Moura (2016) as…”

Section: Generating Heterogeneity Among Units Within the Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modelling Rare Events in an Adaptive Cluster Sampling Design with Heterogeneity among Networks and within the Network Units

Wale-Orojo

Olayiwola

Apantaku

et al. 2022

Sri Lankan J App Stats

View full text Add to dashboard Cite

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Biogeography-Based Optimization for Cluster Analysis

Wang

Chen

et al. 2017

Advances in Computer and Computational Sciences

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Model-Based Inference for Rare and Clustered Populations From Adaptive Cluster Sampling Using Auxiliary Variables

Nolau¹,

Gonçalves²,

Pereira³

2021

Journal of Survey Statistics and Methodology

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Rare populations, such as endangered animals and plants, drug users and individuals with rare diseases, tend to cluster in regions. Adaptive cluster sampling is generally applied to obtain information from clustered and sparse populations since it increases survey effort in areas where the individuals of interest are observed. This work proposes a unit-level model which assumes that counts are related to auxiliary variables, improving the sampling process by assigning different weights to the cells, besides referring to them spatially. The proposed model fits rare and grouped populations arranged on a regular grid in a Bayesian framework. The approach is compared to alternative methods using simulated data and a real experiment in which adaptive samples were drawn from an African buffalo population in a 24,108 square kilometer area in East Africa. Simulation studies show that the model is efficient in several settings, validating the method proposed in this paper for practical situations.

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A Mixture Model for Rare and Clustered Populations Under Adaptive Cluster Sampling

Cited by 4 publications

References 17 publications

Modelling Rare Events in an Adaptive Cluster Sampling Design with Heterogeneity among Networks and within the Network Units

Modelling Rare Events in an Adaptive Cluster Sampling Design with Heterogeneity among Networks and within the Network Units

Biogeography-Based Optimization for Cluster Analysis

Model-Based Inference for Rare and Clustered Populations From Adaptive Cluster Sampling Using Auxiliary Variables

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