An optimum multivariate-multiobjective stratified sampling design

METRON ◽  
2011 ◽  
Vol 69 (3) ◽  
pp. 227-250 ◽  
Author(s):  
Athar Hussain Ansari ◽  
Rahul Varshney ◽  
Najmussehar ◽  
Mohammad Jameel Ahsan
2006 ◽  
Vol 64 (1) ◽  
pp. 97-109 ◽  
Author(s):  
Timothy J. Miller ◽  
John R. Skalski ◽  
James N. Ianelli

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.


2013 ◽  
Vol 19 (3) ◽  
pp. 186 ◽  
Author(s):  
Yeonkook J. Kim ◽  
Yoonhwan Oh ◽  
Sunghoon Park ◽  
Sungzoon Cho ◽  
Hayoung Park

2003 ◽  
Vol 30 (4) ◽  
pp. 331 ◽  
Author(s):  
B. J. Sullivan ◽  
W. M. Norris ◽  
G. S. Baxter

This study used faecal pellets to investigate the broadscale distribution and diet of koalas in the mulgalands biogeographic region of south-west Queensland. Koala distribution was determined by conducting faecal pellet searches within a 30-cm radius of the base of eucalypts on 149 belt transects, located using a multi-scaled stratified sampling design. Cuticular analysis of pellets collected from 22 of these sites was conducted to identify the dietary composition of koalas within the region. Our data suggest that koala distribution is concentrated in the northern and more easterly regions of the study area, and appears to be strongly linked with annual rainfall. Over 50% of our koala records were obtained from non-riverine communities, indicating that koalas in the study area are not primarily restricted to riverine communities, as has frequently been suggested. Cuticular analysis indicates that more than 90% of koala diet within the region consists of five eucalypt species. Our data highlights the importance of residual Tertiary landforms to koala conservation in the region.


2017 ◽  
Vol 10 (1) ◽  
pp. 11-17
Author(s):  
M. A Lone ◽  
S. A Mir ◽  
Imran Khan ◽  
M. S Wani

This article deals with the problem of finding an optimal allocation of sample sizes in stratified sampling design to minimize the cost function. In this paper the iterative procedure of Rosen’s Gradient projection method is used to solve the Non linear programming problem (NLPP), when a non integer solution is obtained after solving the NLPP then Branch and Bound method provides an integer solution.


2012 ◽  
Vol 30 (1) ◽  
pp. 65
Author(s):  
Ummatul Fatima ◽  
Shazia Ghufran ◽  
M. J. Ahsan

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.


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