A Guide to the Fortran Programs to Calculate Inclusion Probabilities for Conditional Poisson Sampling and Pareto πps Sampling Designs

2004 ◽  
Vol 19 (3) ◽  
pp. 337-345 ◽  
Author(s):  
Nibia Aires
Keyword(s):  
Poisson Sampling ◽  
Inclusion Probabilities ◽  
Conditional Poisson Sampling ◽  
Pareto Πps Sampling ◽  
Conditional Poisson

Pareto Sampling versus Sampford and Conditional Poisson Sampling

2006 ◽  
Vol 33 (4) ◽  
pp. 699-720 ◽  
Author(s):  
LENNART BONDESSON ◽  
IMBI TRAAT ◽  
ANDERS LUNDQVIST
Keyword(s):  
Poisson Sampling ◽  
Conditional Poisson Sampling ◽  
Conditional Poisson

scholarly journals Coordination of Conditional Poisson Samples

2015 ◽  
Vol 31 (4) ◽  
pp. 649-672 ◽  
Author(s):  
Anton Grafström ◽  
Alina Matei
Keyword(s):  
Sampling Design ◽  
Random Numbers ◽  
Fixed Size ◽  
Poisson Sampling ◽  
New Methods ◽  
Inclusion Probabilities ◽  
Two Samples ◽  
Sequential Implementation ◽  
Coordination Degree ◽  
Conditional Poisson

Abstract Sample coordination seeks to maximize or to minimize the overlap of two or more samples. The former is known as positive coordination, and the latter as negative coordination. Positive coordination is mainly used for estimation purposes and to reduce data collection costs. Negative coordination is mainly performed to diminish the response burden of the sampled units. Poisson sampling design with permanent random numbers provides an optimum coordination degree of two or more samples. The size of a Poisson sample is, however, random. Conditional Poisson (CP) sampling is a modification of the classical Poisson sampling that produces a fixed-size πps sample. We introduce two methods to coordinate Conditional Poisson samples over time or simultaneously. The first one uses permanent random numbers and the list-sequential implementation of CP sampling. The second method uses a CP sample in the first selection and provides an approximate one in the second selection because the prescribed inclusion probabilities are not respected exactly. The methods are evaluated using the size of the expected sample overlap, and are compared with their competitors using Monte Carlo simulation. The new methods provide a good coordination degree of two samples, close to the performance of Poisson sampling with permanent random numbers.

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.

Asymptotic results for a conditional Poisson log-linear model

1989 ◽  
Vol 7 (4) ◽  
pp. 267-270 ◽  
Author(s):  
Douglas G Bonett ◽  
P.M Bentler ◽  
J.Arthur Woodward
Keyword(s):  
Linear Model ◽  
Asymptotic Results ◽  
Log Linear ◽  
Conditional Poisson
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