Random vs. systematic sampling J. Gallego, MARS AGRI4CAST.
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Transcript of Random vs. systematic sampling J. Gallego, MARS AGRI4CAST.
Random vs. systematic sampling
J. Gallego, MARS AGRI4CAST
Geographic systematic sampling
Positive• More efficient than random sampling if the
spatial autocorrelation is a decreasing function of the distance
• More difficult to manipulate: it gives more confidence • Important if the results are politically sensitive
Geographic systematic sampling (2)
Negative• It may introduce a distortion in the variance if the
landscape is repetitive • Chess board effect if the size and orientation of the cells is the
same as the sampling step• This can be an issue in small pilot regions• Unlikely in large complex regions
• There is no unbiased estimator of the variance• The usual variance estimators are conservative (overestimate
the variance)
Variance estimation in LUCAS
• Usual variance estimator for two-phase random sampling (incomplete stratification)
22
1
11dsth
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hh
hhdst yyw
nN
nN
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2
hh
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hh nn
yy
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• The estimated variance of Y in stratum h can be written
• This estimator is strongly biased for systematic sampling • The bias is reduced with a local estimator of the variance of Y:
jiij
jijiij
hh
yy
fs
21
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otherwise
hijjidij
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stratumintopointsclosest8thein,1
Example of sophisticated sampling scheme
• TREES-2: estimation of tropical deforestation• Very efficient in terms of variance, but strongly attacked in the political
forum
Alternative by FAO for general land cover monitoring
One tile of 10x10 or 20x20 km each lat-long degree
• Half-rate above 60º• Problem of bias
• Less problematic in tropical countries