Bayesian Optimization in Sampling Finite Populations

Rao, J. N. K.; Ghangurde, P. D.

doi:10.1080/01621459.1972.10482406

Cited by 14 publications

(8 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is possible to obtain a point estimate with an estimated variance from the survey for the parameters of interest, and using the estimates to form a prior distribution. This was used by Rao and Ghangurde (1972) for Bayesian optimization in sampling finite populations.…”

Section: Bayesian Empirical Likelihood Inferencementioning

confidence: 99%

Bayesian Empirical Likelihood Inference with Complex Survey Data

Zhao

Ghosh

Rao

et al. 2019

Journal of the Royal Statistical Society Series B: Statistical Methodology

Self Cite

View full text Add to dashboard Cite

Summary We propose a Bayesian empirical likelihood approach to survey data analysis on a vector of finite population parameters defined through estimating equations. Our method allows overidentified estimating equation systems and is applicable to both smooth and non‐differentiable estimating functions. Our proposed Bayesian estimator is design consistent for general sampling designs and the Bayesian credible intervals are calibrated in the sense of having asymptotically valid design‐based frequentist properties under single‐stage unequal probability sampling designs with small sampling fractions. Large sample properties of the Bayesian inference proposed are established for both non‐informative and informative priors under the design‐based framework. We also propose a Bayesian model selection procedure with complex survey data and show that it works for general sampling designs. An efficient Markov chain Monte Carlo procedure is described for the required computation of the posterior distribution for general vector parameters. Simulation studies and an application to a real survey data set are included to examine the finite sample performances of the methods proposed as well as the effect of different types of prior and different types of sampling design.

show abstract

Section: Bayesian Empirical Likelihood Inferencementioning

confidence: 99%

Bayesian Empirical Likelihood Inference with Complex Survey Data

Zhao

Ghosh

Rao

et al. 2019

Journal of the Royal Statistical Society Series B: Statistical Methodology

Self Cite

View full text Add to dashboard Cite

show abstract

“…The following algorithm is an adaptation of the optimization technique used by Draper and Guttman (1968) and Rao and Ghangurde (1972) in a related Bayesian optimization problem; minor modifications in Rao and Ghangurde's Bayesian algorithm yields my algorithm.…”

Section: Appendix: Computation Of the Optimal Second-phase Subsamplinmentioning

confidence: 99%

Is Two-Phase Sampling Really Better for Estimating Age Composition?

Smith

1989

Journal of the American Statistical Association

View full text Add to dashboard Cite

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. American Statistical Association is collaborating with JSTOR to digitize, preserve and extend access to Journal of the American Statistical Association.The two-phase sampling design has been traditionally used in ecology applications to estimate age composition. With regard to both economic and statistical considerations, however, the two-phase design may yield estimates of the age composition that are no better than can be obtained using simple random sampling. This article shows that the optimality of the two-phase sampling design depends upon (a) survey objectives, (b) per-unit sampling costs, and (c) the predictive power of stratum membership on age. Methods are given for determining whether two-phase sampling yields better estimates of the age composition than simple random sampling, accounting for (a), (b), and (c).

show abstract

“…This algorithm is a modification of a Bayesian optimization technique (Draper and Guttman, 1968;Rao and Ghangurde, 1972)and is adapted for our classical statistical framework requiring 1~n,~E(nn in comparison with the Bayesian framework (Smith, 1979;Smith and Sedransk, 1982;linn et al, 1987) requiring 0~n,~E(ni).…”

Section: Appendix B: Computation Of Optimal Second-phase Subsample Sizesmentioning

confidence: 99%

Survey Design Optimization for Estimating the Exploitable Biomass of a Fishery Accounting for Non-Sampling Errors

Smith

1988

Applied Statistics

View full text Add to dashboard Cite

SUMMARY To manage a large commercial fishery it is necessary to have a good estimate of its exploitable biomass. Knowledge of this parameter is crucial since the fishery may be developed, preserved and managed by establishing catch limits and season length according to exploitable biomass. Great economic loss of opportunity and sudden decline in the stock may be consequences of management decisions based on poor estimates of the exploitable biomass. This paper presents methods for determining an optimal survey sampling strategy with the objective of estimating exploitable biomass. In particular, alternative sampling strategies are considered that account specifically for non‐sampling errors, e.g. incorrect aging, that occur in estimating the distribution of catch at age required for estimating exploitable biomass. Our results are important because they account for anticipated non‐sampling errors and utilize important biological notions about how the dynamics of the fishery affect exploitable biomass in specifying an optimal survey sampling strategy. A real application involving Pacific halibut is given to illustrate the methodology.

show abstract

Bayesian Optimization in Sampling Finite Populations

Cited by 14 publications

References 12 publications

Bayesian Empirical Likelihood Inference with Complex Survey Data

Bayesian Empirical Likelihood Inference with Complex Survey Data

Is Two-Phase Sampling Really Better for Estimating Age Composition?

Survey Design Optimization for Estimating the Exploitable Biomass of a Fishery Accounting for Non-Sampling Errors

Contact Info

Product

Resources

About