Statistical Matching and Finite Populations

2006 ◽  
pp. 149-162
Keyword(s):  
Finite Populations ◽  
Statistical Matching

scholarly journals Recombination Can Evolve in Large Finite Populations Given Selection on Sufficient Loci

Genetics ◽  
2003 ◽  
Vol 165 (4) ◽  
pp. 2249-2258 ◽  
Author(s):  
Mark M Iles ◽  
Kevin Walters ◽  
Chris Cannings
Keyword(s):  
Experimental Evidence ◽  
Genetic Drift ◽  
Finite Population ◽  
Selective Advantage ◽  
Indirect Selection ◽  
Small Populations ◽  
Finite Populations ◽  
Population Sizes ◽  
Population Recombination

AbstractIt is well known that an allele causing increased recombination is expected to proliferate as a result of genetic drift in a finite population undergoing selection, without requiring other mechanisms. This is supported by recent simulations apparently demonstrating that, in small populations, drift is more important than epistasis in increasing recombination, with this effect disappearing in larger finite populations. However, recent experimental evidence finds a greater advantage for recombination in larger populations. These results are reconciled by demonstrating through simulation without epistasis that for m loci recombination has an appreciable selective advantage over a range of population sizes (am, bm). bm increases steadily with m while am remains fairly static. Thus, however large the finite population, if selection acts on sufficiently many loci, an allele that increases recombination is selected for. We show that as selection acts on our finite population, recombination increases the variance in expected log fitness, causing indirect selection on a recombination-modifying locus. This effect is enhanced in those populations with more loci because the variance in phenotypic fitnesses in relation to the possible range will be smaller. Thus fixation of a particular haplotype is less likely to occur, increasing the advantage of recombination.

Addressing data deficiencies in assistive technology by using statistical matching methodology: a case study from Malawi

2020 ◽  
pp. 1-15
Author(s):  
Monica Jamali-Phiri ◽  
Juba Alyce Kafumba ◽  
Malcolm MacLachlan ◽  
Emma M. Smith ◽  
Ikenna D. Ebuenyi ◽  
...  
Keyword(s):  
Assistive Technology ◽  
Statistical Matching

scholarly journals Bayesian Analysis of Finite Populations under Simple Random Sampling

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 318
Author(s):  
Manuel Mendoza ◽  
Alberto Contreras-Cristán ◽  
Eduardo Gutiérrez-Peña
Keyword(s):  
Bayesian Analysis ◽  
Random Sampling ◽  
Simple Random Sampling ◽  
Survey Sampling ◽  
Finite Populations ◽  
Population Means ◽  
Sampling Schemes ◽  
Nonparametric Approach ◽  
Conventional Procedure ◽  
Continuous And Discrete Variables

Statistical methods to produce inferences based on samples from finite populations have been available for at least 70 years. Topics such as Survey Sampling and Sampling Theory have become part of the mainstream of the statistical methodology. A wide variety of sampling schemes as well as estimators are now part of the statistical folklore. On the other hand, while the Bayesian approach is now a well-established paradigm with implications in almost every field of the statistical arena, there does not seem to exist a conventional procedure—able to deal with both continuous and discrete variables—that can be used as a kind of default for Bayesian survey sampling, even in the simple random sampling case. In this paper, the Bayesian analysis of samples from finite populations is discussed, its relationship with the notion of superpopulation is reviewed, and a nonparametric approach is proposed. Our proposal can produce inferences for population quantiles and similar quantities of interest in the same way as for population means and totals. Moreover, it can provide results relatively quickly, which may prove crucial in certain contexts such as the analysis of quick counts in electoral settings.

Bootstrap Methods for Finite Populations

1994 ◽  
Vol 89 (428) ◽  
pp. 1282-1289 ◽  
Author(s):  
James G. Booth ◽  
Ronald W. Butler ◽  
Peter Hall
Keyword(s):  
Finite Populations ◽  
Bootstrap Methods
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