scholarly journals Optimal inclusion probabilities for balanced sampling

2011 ◽  
Vol 141 (2) ◽  
pp. 984-994 ◽  
Author(s):  
G. Chauvet ◽  
D. Bonnéry ◽  
J.-C. Deville
Keyword(s):  
Balanced Sampling ◽  
Inclusion Probabilities

scholarly journals A simulated annealing-based algorithm for selecting balanced samples

2021 ◽  
Author(s):  
Roberto Benedetti ◽  
Maria Michela Dickson ◽  
Giuseppe Espa ◽  
Francesco Pantalone ◽  
Federica Piersimoni
Keyword(s):  
Simulated Annealing ◽  
Optimization Problem ◽  
Sample Selection ◽  
Auxiliary Information ◽  
Real Data ◽  
Simulation Experiments ◽  
Balanced Sampling ◽  
Inclusion Probabilities ◽  
Random Method ◽  
Annealing Algorithms

AbstractBalanced sampling is a random method for sample selection, the use of which is preferable when auxiliary information is available for all units of a population. However, implementing balanced sampling can be a challenging task, and this is due in part to the computational efforts required and the necessity to respect balancing constraints and inclusion probabilities. In the present paper, a new algorithm for selecting balanced samples is proposed. This method is inspired by simulated annealing algorithms, as a balanced sample selection can be interpreted as an optimization problem. A set of simulation experiments and an example using real data shows the efficiency and the accuracy of the proposed algorithm.

Construction of Some Non-Invariant Balanced Sampling Designs

1982 ◽  
Vol 31 (3-4) ◽  
pp. 165-184
Author(s):  
S. Sengupta
Keyword(s):  
Fixed Size ◽  
Balanced Sampling ◽  
Inclusion Probabilities ◽  
Selection Of

The problem considered in this paper is that of construcHng fixed size sampling designs having constant inclusion probabilities of first two orders. Some such sampling designs are developed in situations where the population units are subject to classification in one or more ways eliminating the chance of selection of samples for which the units have too lopsided a distribution over the classes.

scholarly journals Balanced sampling of boxes from batches for assessing quality of fruits and vegetables in EU countries

2021 ◽  
Author(s):  
Sara Franceschi ◽  
Gianni Betti ◽  
Lorenzo Fattorini ◽  
Francesca Gagliardi ◽  
Gianni Montrone
Keyword(s):  
Simulation Study ◽  
Fruits And Vegetables ◽  
Central Italy ◽  
Statistical Properties ◽  
Distribution Center ◽  
Balanced Sampling ◽  
Eu Countries ◽  
Inclusion Probabilities

AbstractThe best evaluation for the proportion of defective units in a batch of fruits and vegetables can be achieved by an exhaustive checking of all the boxes in the batch, that is prohibitive to perform in most cases. Usually, only a sample of boxes is checked. In EU countries, EU regulations establish to estimate the proportion of defective units in a batch by the proportion of defective units in the sample, without giving any rule for selecting boxes. Therefore, results are highly dependent on the subjective choice of boxes. In the present study, an objective design-based approach is considered to select boxes from batches, adopting balanced spatial schemes with equal inclusion probabilities. The schemes are able to select samples of boxes evenly spread throughout the batch also ensuring good statistical properties for the proportion of defective units in the sample as estimator of the proportion of defective units in the batch. The performance of these strategies is evaluated by means of a simulation study performed on real and artificial batches of apples, peppers and strawberries. A case study is considered to estimate the proportion of defective units in a batch of courgettes stored in a distribution center of a supermarket chain in Central Italy.

On the Construction of Non­Invariant Balanced Sampling Designs

1979 ◽  
Vol 28 (1-4) ◽  
pp. 109-124 ◽  
Author(s):  
S. Sengupta
Keyword(s):  
Effective Size ◽  
Sample Sizes ◽  
Balanced Sampling ◽  
Inclusion Probabilities

The problem of constructing non­invariant balanced sampling designs of fixed effective size has been considered and solutions have been suggested to cover all possible combinations of values of the population and the sample sizes, The methods also illustrate that fixed effective size designs can easily be constructed with constant inclusion probabilities of first two orders and with the number of possible samples much smaller than that for the usual SRSWOR procedure.

scholarly journals Enhanced cube implementation for highly stratified population

2021 ◽  
Author(s):  
Raphaël Jauslin ◽  
Esther Eustache ◽  
Yves Tillé
Keyword(s):  
Sampling Design ◽  
Auxiliary Information ◽  
New Method ◽  
Balanced Sampling ◽  
Sampling Process ◽  
Inclusion Probabilities ◽  
Stratified Structure ◽  
Selection Of

AbstractA balanced sampling design should always be the adopted strategy if auxiliary information is available. In addition, integrating a stratified structure of the population in the sampling process can considerably reduce the variance of the estimators. We propose here a new method to handle the selection of a balanced sample in a highly stratified population. The method improves substantially the commonly used sampling designs and reduces the time-consuming problem that could arise if inclusion probabilities within strata do not sum to an integer.

scholarly journals Sampling design for the World Health Survey in Brazil

2005 ◽  
Vol 21 (suppl 1) ◽  
pp. S89-S99 ◽  
Author(s):  
Mauricio Teixeira Leite de Vasconcellos ◽  
Pedro Luis do Nascimento Silva ◽  
Célia Landmann Szwarcwald
Keyword(s):  
Health Survey ◽  
Calibration Procedure ◽  
World Health ◽  
Sample Design ◽  
World Health Survey ◽  
Inclusion Probabilities ◽  
The World ◽  
Urban Rural ◽  
Regional Estimates ◽  
Respective Number

This paper describes the sample design used in the Brazilian application of the World Health Survey. The sample was selected in three stages. First, the census tracts were allocated in six strata defined by their urban/rural situation and population groups of the municipalities (counties). The tracts were selected using probabilities proportional to the respective number of households. In the second stage, households were selected with equiprobability using an inverse sample design to ensure 20 households interviewed per tract. In the last stage, one adult (18 years or older) per household was selected with equiprobability to answer the majority of the questionnaire. Sample weights were based on the inverse of the inclusion probabilities in the sample. To reduce bias in regional estimates, a household weighting calibration procedure was used to reduce sample bias in relation to income, sex, and age group.

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