Page 326 - Contributed Paper Session (CPS) - Volume 6

CPS1942 Daniel D. M. P.
2. When sample size is small, bias of PPAS and PPSS estimates are
roughly the same.
3. SRSWOR estimates have lesser bias then PPAS and PPSS, especially in
populations with small variability.
4. The optimality of PPAS estimates improve as the linear association
between the target variable and auxiliary variable increase.
5. PPAS estimates are more stable under large variability in population as
compared to SRWOR.
The findings above may only reflect the simulated data and may not
necessarily be true for other random generators. It is advisable to verify these
findings by recreating the data using different random seeds used in the study.
A similar study may also be conducted to explore on other non-linear
relationships between the target and auxiliary variable.

References
1. Barrios E. & Kwong, A. H. (2010) Nonparametric Model-Based Estimation in
Data Mining. 11 National Convention on Statistics. EDSA Shangri-La Hotel.
th
2. Efron B. (1992) Bootstrap Methods: Another Look at the Jackknife. In: Kotz
S., Johnson N.L. (eds) Breakthroughs in Statistics. Springer Series in
Statistics (Perspectives in Statistics). Springer, New York, NY
3. Gauran, I. & Poblador, M. (2012) Sampling with Probability Proportional to
Aggregate Size using Nonparametric Bootstrap in Estimating Total
Production Area of Top Cereals and Root Crops across Philippine Regions.
The Philippine Statistician Vol. 61, No. 1, pp. 87-108
nd
4. Lohr, S. 2010. Sampling Design and Analysis, 2 Ed. Boston: Brooks/Cole,
p. 147.

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