is in Appendix C.)

We know that the properties of the estimates will depend on the design method. The D-optimal design criterion selects observations which maximize the determinant of the Fisher information matrix (or, equivalently, minimize the volume of the confidence ellipsoid for the parameters) [@Neyer1994; @Silvey1980]. Because the selection is based on the current estimates of the unknown parameter for each procedure, the candidate observations for each procedure are usually different. In addition, we adopt a ‘without replacement’ principal, where each observation will be recruited by one procedure only.

Computation matter
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Active learning is a special case of machine learning, and it is usually operated in a sequential way. From a statistical viewpoint, this type of method approximates a general sequential method and a statistical experimental design. When applying active learning methods under ‘big data’ scenarios, we usually assume that the data set used in the analysis already exists. Hence, how to efficiently find informative observations sequentially, such that we can efficiently and effectively conduct the analysis, is an important research problem. In this situation, design criteria are usually used to select the promising candidates from the existing data, rather than randomly select them. The work of [@Deng2009] 