The scoring algorithm is an iterative method for numerically determining the maximum likelihood estimator. Subsampling is an alternative method for approximating the sampling distribution of an estimator. The Inverse Probability Weighted Estimator (IPWE) can be unstable if estimated propensities are small. It is furthermore equal to the conditional maximum likelihood estimator. This model holds the same assumptions as the Inverse Probability Weighted Estimator (IPWE). This provides a improvement, and was incorporated in the overlapping Allan variance estimator. However the maximum likelihood estimator is third-order efficient. Sometimes the words "estimator" and "estimate" are used interchangeably. For example, for the maximum likelihood estimator, formula 17, where formula 18. The Kalman filter is a recursive estimator. Thus the kernel density estimator coincides with the characteristic function density estimator. The posterior median is attractive as a robust estimator. The Kalman filter is a minimum mean-square error estimator. Thus the "maximum likelihood estimator" for "p" is . An estimator or decision rule with zero bias is called unbiased.