(\[FRn\]), especially when $p$ is not overly large.

In most ‘big data’ analysis scenarios, data have already been collected and are stored in some digital device, such that we can easily manage and access the data: we simply ‘pick out’ those desired observations from the available data pool, based on a predetermined selection criterion (in our method, $D$-optimality). The ‘sampling’ cost here is lower than that in conventional situations, since there is no need to conduct new experiments or data collection processes.

Adaptive shrinkage estimation {#sec:ASE}
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Model interpretation is essential in most data analysis scenarios. Users always prefer to have solutions that are interpretable and understandable. Additionally, researchers can always benefit from the interpretability of a model to validate and/or further improve their hypotheses. By adding the ASE feature to the distributed sequential estimation procedure, we can better detect variables that significantly affect the model, and thereby increase the interpretability of the model. This is especially the case when the data set at hand contains a long list of variables. In this section, we provide a definition for ASE, such that the proposed procedure can detect the effective variables for the model during its estimation process.

Suppose that only 