TY - UNPB
T1 - Vanishing auxiliary variables in PPS sampling - with applications in microscopy
AU - Andersen, Ina Trolle
AU - Hahn, Ute
AU - Jensen, Eva B. Vedel
PY - 2014/2
Y1 - 2014/2
N2 - Recently, non-uniform sampling has been suggested in microscopy to increase efficiency. More precisely, sampling proportional to size (PPS) has been introduced where the probability of sampling a unit in the population is proportional to the value of an auxiliary variable. Unfortunately, vanishing auxiliary variables are a common phenomenon in microscopy and, accordingly, part of the population is not accessible, using PPS sampling. We propose a modification of the design, for which an optimal solution can be found, using a model assisted approach. The optimal design has independent interest in sampling theory. We verify robustness of the new approach by numerical results, and we use real data to illustrate the applicability.
AB - Recently, non-uniform sampling has been suggested in microscopy to increase efficiency. More precisely, sampling proportional to size (PPS) has been introduced where the probability of sampling a unit in the population is proportional to the value of an auxiliary variable. Unfortunately, vanishing auxiliary variables are a common phenomenon in microscopy and, accordingly, part of the population is not accessible, using PPS sampling. We propose a modification of the design, for which an optimal solution can be found, using a model assisted approach. The optimal design has independent interest in sampling theory. We verify robustness of the new approach by numerical results, and we use real data to illustrate the applicability.
KW - microscopy
KW - model assisted sampling
KW - optimal allocation
KW - proportional regression models
KW - systematic PPS sampling
KW - vanishing auxiliary variables
M3 - Working paper
T3 - CSGB Research Reports
BT - Vanishing auxiliary variables in PPS sampling - with applications in microscopy
PB - Centre for Stochastic Geometry and Advanced Bioimaging, Aarhus University
ER -