Multiple inverse sampling in post-stratification |
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| Institution: | 2. Department of Radiation Oncology, University of North Carolina School of Medicine, Chapel Hill, North Carolina;3. Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, North Carolina;4. Department of Therapeutic Radiology, Yale University School of Medicine, New Haven, Connecticut;6. Department of Otolaryngology-Head and Neck Surgery, Mayo Clinic, Rochester, Minnesota;5. Division of Biomedical Statistics and Informatics, Mayo Clinic, Phoenix, Arizona;11. Division of Medical Oncology, Mayo Clinic, Rochester, Minnesota;12. Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota.;1. School of Electronic and Information Engineering, Beihang University, Group 203, Beijing 100191, China;2. Department of Energy, Polytechnic of Milan, Via Ponzio 34/3, Milan 20133, Italy;3. Ecole Central Paris et Supelec, France;1. Cancer Research Program, FIMIM Hospital del Mar, Barcelona, Spain;2. Medical Oncology Department, Hospital del Mar, Barcelona;3. Traslational Medical Oncology Group (Oncomet)/Liquid Biopsy Analysis Unit, Health Research Institute of Santiago (IDIS), Complexo Hospitalario Universitario de Santiago de Compostela (SERGAS) CIBERONC, Santiago de Compostela;4. Pathology Department, Hospital del Mar, Barcelona;5. Sysmex Inostics Inc., Mundelein, USA;6. Universitat Pompeu Fabra, Barcelona, Spain |
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| Abstract: | In sample survey, post-stratification is often used when the identification of stratum cannot be achieved in advance of the survey. If the sample size is large, post-stratification is usually as effective as the ordinary stratification with proportional allocation. However, in the case of small samples, no general acceptable theory or technique has been well developed. One of the main difficulties is the possibility of obtaining zero sample sizes in some strata for small samples. In this paper, we overcome this difficulty by employing a sampling scheme referred to as the multiple inverse sampling such that each stratum is ensured to be sampled a specified number of observations. A Monte Carlo simulation is carried out to compare the estimator obtained from the multiple inverse sampling with some other existing estimators. The estimator under multiple inverse sampling is superior in the sense that it is unbiased and its variance does not depend on the values of stratum means in the population. |
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