A guide to the Fortran programs to calculate inclusion probabilities for conditional Poisson Sampling and Pareto πps Sampling designs

Computational Statistics

Volumn 19, Issue 3, 2004, Pages 337-345

  Aires, Nibia ^a  

^a HALMSTAD UNIVERSITY   (Sweden)

Author keywords

Algorithms; Conditional Poisson Sampling; Numerical Integration; Pareto ps Sampling; Sampling Theory

Indexed keywords

EID: 4544271545     PISSN: 09434062     EISSN: None     Source Type: Journal    
DOI: 10.1007/BF03372100     Document Type: Article

References (9)

1. Aires, N. (1999). Algorithms to find exact inclusion probabilities for Conditional Poisson Sampling and Pareto πps Sampling designs, Methodology and Computing in Applied Probability, 1:4, pp. 457-469.
2. Aires, N. (2000). Comparisons between Conditional Poisson Sampling and Pareto πps Sampling designs, Journal of Statistical Planning and Inference, 88, pp. 133-147.
3. Aires N, Rosén B. (2000). On inclusion probabilities and estimator bias for Pareto πps Sampling, Statistics Sweden R & D report 2000:2.
4. Atkinson, K. (1988). An introduction to numerical analysis, 2nd. ed., Wiley, Singapore.
5. Hájek, J. (1964). Asymptotic theory of Rejective Sampling with varying probabilities from a finite population. Annals of Mathematical Statistics. 35, pp. 1491-1523.
6. Ohlsson, E. (1995). Sequential Poisson Sampling, Institute of actuarial Mathematics and Mathematical Statistics, Stockholm University, Report No. 182.
7. Rosén, B. (1997a). Asymptotic theory for Order Sampling, Journal of Statistical Planning and Inference, 62, 135-158.
8. Rosén, B. (1997b). On sampling with probability proportional to size, Journal of Statistical Planning and Inference, 62, 159-191.
9. Saavedra (1995). Fixed sample size PPS approximations with a permanent random number. 1995 Joint Statistical Meetings American Statistical Association. Orlando, Florida.