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An optimal residual generation with no modeling errors is used as the reference residual model of robust fault detection filter design for the uncertain discrete-time systems with both modeling errors and SNR constraints; then, the robust fault detection filter design is formulated as an optimal H∞ filtering problem.

The sum of squared errors is used as classification criteria.

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Defect integrals or local truncation errors are used as local error quantities.

In the analysis, varve counts and its errors are used as prior probability, and age of control points (Table 1) are used for constraints.

The numbers of achieved categories and perseverative errors were used as MCST indices.

Maximum likelihood robust (MLR) standard errors was used as the estimation method for all analyses.

These standard errors were used as weights for summary effect estimates in the meta-analysis.

These OOB errors were used as a non-biased indicator of the performance of the classifiers.

Total errors were used as an index of working memory manipulation.

Since there were some mild violations, robust standard errors were used as recommended by Cameron and Trivedi (2009).

Model based variance estimator for the standard errors was used as recommended in the presence of few large clusters [ 26].

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