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A double-population MA is proposed, where the position population is used to evaluate the ranges, and the coefficient population is used to realize a local search of target reflectivities.
These models included: logistic regression for ignoring correlation, logistic regression weighted by the intra cluster correlation coefficient, population average logistic regression using generalized estimating equations (GEE), and random effects logistic regression.
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The parameters depend on the particular model and usually involve effective population sizes, migration rates, splitting times, admixture coefficients, population growth rates, etc.
With regards to the relationship between inbreeding depression and inbreeding coefficient, the population size that minimizes inbreeding depression depends on the level of inbreeding: inbreeding depression can even decrease when population size increases.
The authors also test correlations with other indicators such as the Gini coefficient, youth population, unemployment rates, poverty and health.
The IV coefficient on population is negative, but small and insignificant.
For other control variables, the coefficient of population is significantly negative and that of land-area size is significantly positive.
The regression also produces a positive coefficient for population density, a positive coefficient for the physical size of the area, and a negative coefficient for the average size of the local hospitals all of which are significant at conventional levels.
The final selection coefficient per population was calculated as the average of the two blocks.
As with both previous models, the interaction term drastically changes the coefficient of population density in districts with a city.
Therefore, we expect our algorithm to overestimate the inbreeding coefficient when population structure in the genotype data is not taken into account for the estimation.
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