Sentence examples for bias formulae from inspiring English sources

Asymptotic distributions and bias formulae are obtained for estimates of the mean reversion parameter.

Simulations suggest that when the mean reversion is slow the approximation methods work better than ML, the bias formulae are accurate, and for scalar models the estimates obtained from the two approximate methods have smaller bias and variance than exact ML.

Implementation of the methods requires numerical evaluation of the appropriate bias formulae.

(1998) proposed bias formulae for survival analysis with unmeasured confounders based on the assumption of rare events (small ) or small.

A simulation study is used to demonstrate the validity of the bias formulae and to characterize the influence of the different sources of bias.

The bias formulae are used as the basis for developing a new method of sensitivity analysis to assess the impact of omitted covariates on estimates of treatment or exposure effects.

Such empirical findings agree with the bias formulas for logistic regression in the work of Carroll et al. (1995).

The bias formula was used as the basis for proposing a new method to assess the sensitivity of estimates of treatment effects to omission of relevant covariates.

The assumption is required to calculate the expectation in Eq. (3) for each study to derive the omitted variable bias formula, γ ∗= f (α, β, p XZ ).

The function f comes from the omitted variable bias formula introduced in the previous section, whose formulation is reasonable if an assumption of homogeneity of studies in meta-analysis is acceptable.

Their approach utilizes the reparameterization connecting each study-specific parameter to the common parameter using the transformation function M i, which is used as the omitted variable bias formula in our setting.