Most work on the human fertility transition has focused on declines in mean fertility. However, understanding changes in the variance of reproductive outcomes can be equally important for evolutionary questions about the heritability of fertility, individual determinants of fertility and changing patterns of reproductive skew. Here, we document how variance in completed fertility among women (4...

In contrast to pure metals and most non-glass forming alloys, metallic glass-formers are moderately strong liquids in terms of fragility. The notion of fragility of an undercooling liquid reflects the sensitivity of the viscosity of the liquid to temperature changes and describes the degree of departure of the liquid kinetics from the Arrhenius equation. In general, the fragility of metallic gl...

We investigate methods for variance reduction and the elimination of systematic error in a Fourier accelerated Langevin scheme for general spin models. We present results for the SU (3) × SU (3)/SU (3) model in two-dimensions that are consistent with those from multi-grid methods. We argue that the timing for the Langevin method makes it comparable to multi-grid for a given level of error.

Analyses of individual disease-exposure data within a population are useful when exposure of interest varies sufficiently within the population. When the within-population variance of exposure is limited, however, power of the individual-data analysis is reduced. In such situations, aggregated-data analyses of disease data across populations, with a sample of individual exposure data from each ...

On-line analysis of output data from discrete event stochastic simulation focuses almost entirely on estimation of means. Most “variance estimation” research in simulation refers to the estimation of the variance of the mean, to construct confidence intervals for mean values. There has been little research on the estimation of variance in simulation. We investigate three methods for point and i...

Table 1: Four simulation scenarios used in the evaluation of the bias-variance decomposition. The simulation scenarios are taken from Zou & Hastie (2005). scenario n p β Structure of X (1) 100 8 (3, 1.5, 0, 0, 2, 0, 0, 0) corr (i, j) = 0.5|i−j| (2) 100 8 0.85 for all j corr (i, j) = 0.5|i−j| (3) 50 40 βj = { 0 j = (1, . . . , 10, 21, . . . , 30) 1 j = (11, . . . , 20, 31, . . . , 40) corr (i, j...