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Exact(4)
We assume a finite population model, where the population element is the land associated with each 230-meter pixel.
Turning to the mean square error of the predictor in Eq. (6) we need to acknowledge that uncertainty is introduced both by the estimation of the model parameters and by the random residual terms linked to each population element.
We consider the set of meadows as a metapopulation system where each population is a meadow and each population element is an n-dimensional vector representing an equiprobable gamete of that meadow, where n is the number of loci.
where s is the sample of auxiliary data, π i is the probability of including population element i into the auxiliary data sample, π is an n-length column vector of (1/π i ) – values, and X is an n × p matrix of sampled auxiliary data.
Similar(56)
Some factors studied were the amount of imperfection in the frame, the nature of the correspondence between the frame units and population elements, the population coefficient of variation, and the correlation of the weights used to correct for the frame's imperfections with the measure of interest.
The values linked to population elements are random variables.
Thus, in the standard case, auxiliary data are available for all population elements.
Therefore, the probability of all population elements belong to the same sample is unknown.
The values that are linked to the population elements are fixed.
Consequently, several measures to overcome that problem were envisaged, namely, a large GA population with elements, the crossover of all population elements and the adoption of elitism, a mutation probability of 10%, and an evolution with iterations.
The subjects were selected through a non probabilist approach by convenience, so that the probability of all population elements belong to the same sample is unknown.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com