The biases reported in the result section are defined as the estimate of the expected value of our covariance estimator minus the Monte Carlo covariance.
Standardized bias is defined as the difference between the expectation of the estimator and the parameter, divided by the standard deviation of the estimator.
The term "bias" denotes all biases and is defined as the difference between the expectation of the estimator and the true average causal effect.
where |F| is the determinant of the Fisher information matrix and εrms is defined as the RMSE of location estimators.
We also show the performance of F based precision calculations for random configurations of multiple emitters by looking at the estimator accuracy, defined as the ratio of the precision calculated using F to the observed standard deviation of the estimates.
The estimator is defined as the minimizer of a minimum distance function that measures the distance between the ranked set sample empirical cumulative distribution function and a possibly misspecified target model.
Here, the breakdown point, a criterion often used to measure robustness of an estimator, is defined as the proportion of incorrect observations (i.e., arbitrarily small or large observations), the estimators of α and β can handle before giving estimated values arbitrary close to zero or infinity.
With a limited number of secondary data set, a class of covariance matrix estimators, defined as the geometric barycenters of some basic covariance matrix estimates obtained from the available secondary data set, are proposed by exploiting the characteristic of the positive-definite matrix space.
That is, the conditional maximum likelihood estimators defined as the maximisers of L F (β F ) and L P (β P ), respectively, can be obtained as separate maximum-likelihood estimates of both binary logit models.
The RMSE of an estimator is defined as the square root of the mean squared error (MSE) of.
The HL estimator is defined as the median of n(n−1)/2 pairwise averages of observations and can be written as begin{array}rcl@ tilde mu = text{median}_{i < j}left{frac{y_{i} + y_{j}}{2}right}, end{array} (5).
