Estimation of Parameters

Abstract The Deng-Lynch method was developed to estimate the rate and effects of deleterious genomic mutations (DGM) in natural populations under the assumption that populations are either completely outcrossing or completely selfing and that populations are at mutation-selection (M-S) balance. However, in many plant and animal populations, selfing or outcrossing is often incomplete in that a proportion of populations undergo inbreeding while the rest are outcrossing. In addition, the degrees of deviation of populations from M-S balance are often not known. Through computer simulations, we investigated the robustness and the applicability of the Deng-Lynch method under different degrees of partial selfing or partial outcrossing and for nonequilibrium populations approaching M-S balance at different stages. The investigation was implemented under constant, variable, and epistatic mutation effects. We found that, generally, the estimation by the Deng-Lynch method is fairly robust if the selfing rate (S) is <0.10 in outcrossing populations and if S > 0.8 in selfing populations. The estimation may be unbiased under partial selfing with variable and epistatic mutation effects in predominantly outcrossing populations. The estimation is fairly robust in nonequilibrium populations at different stages approaching M-S balance. The dynamics of populations approaching M-S balance under various parameters are also studied. Under mutation and selection, populations approach balance at a rapid pace. Generally, it takes 400–2000 generations to reach M-S balance even when starting from homogeneous individuals free of DGM. Our investigation here provides a basis for characterizing DGM in partial selfing or outcrossing populations and for nonequilibrium populations.

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Spatiotemporal Econometrics Models for Old Age Mortality in Europe

Mathematics ◽

10.3390/math9091061 ◽

2021 ◽

Vol 9 (9) ◽

pp. 1061

Author(s):

Patricia Carracedo ◽

Ana Debón

Keyword(s):

Panel Data ◽

Goodness Of Fit ◽

Population Aging ◽

European Population ◽

Data Models ◽

European Countries ◽

Mortality Data ◽

Panel Data Models ◽

Estimation Of Parameters ◽

Advantages And Disadvantages

In the past decade, panel data models using time-series observations of several geographical units have become popular due to the availability of software able to implement them. The aim of this study is an updated comparison of estimation techniques between the implementations of spatiotemporal panel data models across MATLAB and R softwares in order to fit real mortality data. The case study used concerns the male and female mortality of the aged population of European countries. Mortality is quantified with the Comparative Mortality Figure, which is the most suitable statistic for comparing mortality by sex over space when detailed specific mortality is available for each studied population. The spatial dependence between the 26 European countries and their neighbors during 1995–2012 was confirmed through the Global Moran Index and the spatiotemporal panel data models. For this reason, it can be said that mortality in European population aging not only depends on differences in the health systems, which are subject to national discretion but also on supra-national developments. Finally, we conclude that although both programs seem similar, there are some differences in the estimation of parameters and goodness of fit measures being more reliable MATLAB. These differences have been justified by detailing the advantages and disadvantages of using each of them.

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