A Bayesian spatial model with auxiliary covariates to assess and adjust nonignorable nonresponse

Gao, Xiaoming; He, Chi; Sun, Dongchu

doi:10.1016/j.spasta.2013.11.002

Cited by 2 publications

(6 citation statements)

References 23 publications

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“…The final data analysis results show that the phase-county random effect term for nonorespodents has larger variance, and this yields lower satisfaction rates. In Gao et al (2014), the same survey was analyzed by using a different approach with individual covariates; their estimated statewide satisfaction rate of nonrespondents was 0.7100, which is very close to our estimate 0.7092 in this paper. This further indicates our model is reasonable for the data.…”

Section: Logistic Linear Mixed Modelssupporting

confidence: 84%

“…If we ignored nonresponse, we would overestimate the number of satisfied hunters by more than 9,000 statewide. Gao et al (2014) analyzed this 2001 MDHAS using a bivariate Bayesian hierarchical linear mixed model to estimate hunters' satisfaction rate and response rate. Both estimated rates were then aggregated at hunters' county of residence and at the state level respectively.…”

Section: Discussionmentioning

confidence: 99%

“…Conditional autoregressive (CAR) (Besag, 1974) or similar models are used to fit the spatial effects. Gao et al (2014) used a bivariate Bayesian hierarchical lineal mixed model to estimate hunter's satisfaction rate and response rate. The estimated satisfaction and response rates were then aggregated at subdomain and statewide levels.…”

Section: Introductionmentioning

confidence: 99%

“…The estimated satisfaction and response rates were then aggregated at subdomain and statewide levels. Gao et al (2014) explicitly included nonrespondents through auxiliary covariates, but these kind of covariates are not always available or hard to get in mail surveys. Oleson and He (2008) proposed a Bayesian hierarchical model to estimate hunting success rates given response phase and other phase-specific response rates.…”

Section: Introductionmentioning

confidence: 99%

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Adjusting nonresponse bias in small area estimation without covariates via a Bayesian spatial model

Gao

Sun

2014

Statistics and Its Interface

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Sometimes a survey sample is drawn from a large area even if the estimate of interest is at a smaller subdomain level. This strategy, however necessary, may cause small sample problems. The estimation problem is further complicated by survey nonresponse. We build a Bayesian hierarchical spatial model that takes into account both small sample size and nonresponse. This Bayesian model gives the estimates of marginal satisfaction rates at subdomains even when there is no covariate available via modeling the phasespecific response rates and conditional satisfaction rates given response status at subdomains. This method is illustrated using data from the 2001 Missouri Deer Hunter Attitude Survey. Satisfaction, in this survey, refers to whether respondents were satisfied with the Missouri Department of Conservation's deer management program. The estimated satisfaction rates are lower after adjusting for nonresponse bias compared to the satisfaction rates based only on responses.

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Section: Logistic Linear Mixed Modelssupporting

confidence: 84%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adjusting nonresponse bias in small area estimation without covariates via a Bayesian spatial model

Gao

Sun

2014

Statistics and Its Interface

Self Cite

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“…However, this may not be true because successful hunters are more often likely to respond to a hunter survey than unsuccessful hunters (Filion ). Oleson and He () and Gao et al () examined methods that would help address this issue; however, we did not explore their approaches in our analyses.…”

Section: Discussionmentioning

confidence: 99%

Model selection for wild turkey hunter success rates using small‐area estimation methods

Sheriff

Sun

et al. 2018

Wildl. Soc. Bull.

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Wildlife management agencies conduct hunter surveys to obtain information useful for making management decisions. Often these surveys are designed to collect data, such as hunter success rates, with reliable precision at statewide or regional scales. However, at finer spatial resolutions, such as at the county level, sample sizes are often small, making estimates either impossible to calculate or unreliable using traditional statistical procedures. We used Bayesian hierarchical modeling methods to overcome this smallarea estimation problem for the 1996 Missouri, USA, spring wild turkey (Meleagris gallopavo) hunter survey. We explored 8 potential models based on 3 general hypothesized models. These families of general models included a binomial-beta-gamma model, 5 variations of spatial dependency models with and without covariates, and 2 variations of models with covariates but without accounting for spatial dependency. We used Bayes factors to select among models. We selected a conditional autoregressive binomial logistic linear mixed model as the most appropriate and used the posterior predictive distributions from this model to assess its fit. This small-area estimation model provided precise hunter success-rate estimates at the county level even though this hunter survey was designed to provide reliable estimates at the regional-scale. This approach provides estimates at smaller scales than are possible using traditional statistical analyses, allowing managers to identify when small-scale management decisions are warranted. Ó 2018 The Wildlife Society.

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A Bayesian spatial model with auxiliary covariates to assess and adjust nonignorable nonresponse

Cited by 2 publications

References 23 publications

Adjusting nonresponse bias in small area estimation without covariates via a Bayesian spatial model

Adjusting nonresponse bias in small area estimation without covariates via a Bayesian spatial model

Model selection for wild turkey hunter success rates using small‐area estimation methods

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