Premium
Optimal sampling designs for dependent spatial units
Author(s) -
Benedetti Roberto,
Palma Daniela
Publication year - 1995
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.3170060202
Subject(s) - estimator , simulated annealing , mathematics , cardinality (data modeling) , sampling (signal processing) , algorithm , best linear unbiased prediction , statistics , variance (accounting) , mathematical optimization , computer science , data mining , artificial intelligence , accounting , filter (signal processing) , business , computer vision , selection (genetic algorithm)
A geographical domain is partitioned into a set, with cardinality N , of areal units (i.e. census tracts), each of them having an attribute variable z. Observations are often to be recorded for a subset S of areal units whose cardinality is n. Under the hypothesis of dependence of the underlying data generating process Z , the following questions are considered: which is the best linear unbiased estimator (BLUE) of the mean of the process Z , and which is the subset S that minimizes the variance of this estimator? A weighted average estimator is used and the performances of some combinatorial optimization algorithms are tested to solve this problem. The simulated annealing algorithm is shown to be a suitable solution even when dealing with large data sets. Moreover, numerical comparisons are made between sampling designs obtained by using simulated annealing and the classical simple random and systematic sampling criteria.