Multivariate Stratified Surveys

Chatterjee, Samprit

doi:10.2307/2284023

Cited by 36 publications

(12 citation statements)

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“…A given sampling scheme will usually not result in the same precision for the estimates of all variates. This problem has been noted by Steel & Torrie (1960) and by Gerard & Berthet (1971), and indicates the need for a compromise multivariate sampling strategy, in such cases--see Chatterjee (1968) and Cochran (1977, pp. 119-123).…”

Section: Allocation Of Sampling Resourcesmentioning

confidence: 90%

Sampling designs for the estimation of phytoplankton abundance in limnetic environments

Irish

Clarke

1984

British Phycological Journal

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Section: Allocation Of Sampling Resourcesmentioning

confidence: 90%

Sampling designs for the estimation of phytoplankton abundance in limnetic environments

Irish

Clarke

1984

British Phycological Journal

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“…Kokan and khan (1967) have proved the existence and uniqueness of the solution and have given the optimal solution through iterative procedure. Chatterjee (1968) gives an algorithm to solve the problem. In 1960, Rosen developed the Gradient Projection method for linear constraints and later in 1961, generalized it for nonlinear constraints.…”

Section: Formulation Of the Problemmentioning

confidence: 99%

Optimal allocation of stratified sampling design using Gradient Projection method

et al. 2017

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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.

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“…Indeed, Chatterjee (1968), Chatterjee (1972), and Huddleston, Claypool, and Hocking (1970) use the first variant. Multivariate optimal allocation problems are addressed in the same way in Kokan (1963) and supplemented by existence and uniqueness results in Kokan and Khan (1967).…”

Section: Introductionmentioning

confidence: 99%

Multivariate optimal allocation with box-constraints

Friedrich¹,

Münnich²,

Rupp³

2018

AJS

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Modern surveys aim at fostering accurate information on demographic and other variables. The necessity for providing figures on regional levels and on a variety of subclasses leads to fine stratifications of the population. Optimizing the accuracy of stratified random samples requires incorporating a vast amount of strata on various levels of aggregation. Accounting for several variables of interest for the optimization yields a multivariate optimal allocation problem in which practical issues such as cost restrictions or control of sampling fractions have to be considered. Taking advantage of the special structure of the variance functions and applying Pareto optimization, efficient algorithms are developed which allow solving large-scale problems. Additionally, integrality-and box-constraints on the sample sizes are considered. The performance of the algorithms is presented comparatively using an open household dataset illustrating their advantages and relevance for modern surveys.

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Multivariate Stratified Surveys

Cited by 36 publications

References 0 publications

Sampling designs for the estimation of phytoplankton abundance in limnetic environments

Sampling designs for the estimation of phytoplankton abundance in limnetic environments

Optimal allocation of stratified sampling design using Gradient Projection method

Multivariate optimal allocation with box-constraints

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