Optimum allocation in multivariate stratified sampling design in the presence of nonresponse with Gamma cost function

2019 ◽  
Vol 89 (13) ◽  
pp. 2454-2467 ◽  
Author(s):  
R. Varshney ◽  
Mradula
Keyword(s):  
Cost Function ◽  
Sampling Design ◽  
Stratified Sampling ◽  
Optimum Allocation ◽  
Multivariate Stratified Sampling

scholarly journals The problem of optimum allocation in multivariate stratified sampling

2012 ◽  
Vol 30 (1) ◽  
pp. 65
Author(s):  
Ummatul Fatima ◽  
Shazia Ghufran ◽  
M. J. Ahsan
Keyword(s):  
Sampling Design ◽  
Stratified Sampling ◽  
Optimum Allocation ◽  
Multiple Response ◽  
Sample Surveys ◽  
Multivariate Stratified Sampling ◽  
Highly Correlated

Generally, sample surveys are multivariate in nature where multiple response are obtained on every unit selected in a sample, that is, more than one characteristics are defined on each and every unit of the population. While dealing with a multivariate stratified population, to workout an allocation that is optimum for all characteristics is almost impossible unless the characteristics are highly correlated. Some compromise must be allowed to obtain an allocation that is optimum, in some sense, for all the characteristics. Since such allocations are based on some compromise criteria they are known as compromise allocations. This paper deals with the problem of obtaining an optimum allocation in multivariate stratified sampling design.

scholarly journals Optimizing a stratified sampling design when faced with multiple objectives

2006 ◽  
Vol 64 (1) ◽  
pp. 97-109 ◽  
Author(s):  
Timothy J. Miller ◽  
John R. Skalski ◽  
James N. Ianelli
Keyword(s):  
Fish Species ◽  
Quantitative Methods ◽  
Sampling Design ◽  
Multiple Objectives ◽  
Stratified Sampling ◽  
Sampling Effort ◽  
Annual Data ◽  
Sample Sizes ◽  
Analytical Results ◽  
Coverage Rates

Abstract Miller, T. J., Skalski, J. R., and Ianelli, J. N. 2007. Optimizing a stratifield sampling design when faced with multiple objectives – ICES Journal of Marine Science, 64, 97–109. For many stratified sampling designs, the data collected are used by multiple parties with different estimation objectives. Quantitative methods to determine allocation of sampling effort to different strata to satisfy the often disparate estimation objectives are lacking. Analytical results for the sampling fractions and sample sizes for primary units within each stratum of a stratified (multi-stage) sampling design that are optimal with respect to a weighted sum of relative variances for the estimation objectives are presented. Further, an approach for assessing gains or losses for each estimation objective by changing allocation of sample sizes to each stratum is provided. As an illustration, the analytical results are applied to determine optimal observer sampling fractions (coverage rates) for the North Pacific Groundfish Observer Programme (NPGOP), for which the multiple objectives are assumed to be bycatch (seabird, marine mammal, and non-targeted fish species) and total catch, and catch-at-length and -age of targeted fish species. Simultaneously optimizing a criterion that defines the strata of the NPGOP sampling design is also considered. When observer coverage rates are allowed to be gear-specific for the NPGOP design, the optimized objective function is between 10% and 28% less than the value corresponding to current sampling for annual data (2000–2003) and 12% less when optimized over all years combined.

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