Sampling weights generally reduce bias but increase the variance of estimates.
We see that increasing the prior variance of the intercept terms has little impact on the probability of correctly identifying the optimal dose but increasing the variance of the slope decreases the probability of correctly identifying the optimal dose considerably.
Adding to model 5 (including true confounder + variable related to the outcome) a variable related to treatment allocation (model 7) did not reduce the bias, but increased the variance of the estimation.
Moreover, adding to the PS model a variable related neither to the treatment nor to the outcome (model 8), did not further improve the precision of the estimation, but increased the variance, especially when the sample size decreased below 100.
However, this theory fails in explaining the effectiveness of AdaBoost which constantly improves the generalization performance by adding more base classifiers but it increases the variance.
Reducing the span reduces the bias but also increases the variance (reducing smoothness).
Hence, selection for BW increases the variance of BW, but decreases the coefficient of variation (CV) for BW because the increase in variance of BW is smaller than expected if the CV for BW remains constant or decreases.
Likewise, driving too slowly increases the variance in the speed of vehicles traveling together, which increases the risk of accidents (Garber & Gadirau, 1988).
ω i increases the variance of likelihood in CI fusion.
The exposure measurement error contains a Berkson-like component that increases the variance of the effect estimate and a classical-type component that not only increase the variance but also bias the effect estimates [ 64].
