been applied to many areas since then, including clinical trials, finance, engineering control, and educational and psychological tests, *inter alia*. The major feature of the sequential method is that it allows the sample size to be random and depend on the observed information [@Chow1965; @Woodroofe1982; @whitehead97; @Bartroff2013]. In addition to the random sample sizes, some sequential methods allow users to recruit new observations, based on information gathered while analysing the current-stage data. This type of sequential method is common in the literature on stochastic control, educational test, active learning in machine learning, and the like [@Lai1982; @Lord1971; @Wainer2000; @Deng2009]. The identification of important variables is a critical feature in applications, especially where the ability to interpret model results is essential and the data set has a lengthy list of variables. To this end, we adopt the adaptive shrinkage estimate (ASE) of [@wangchang13], such that we can effectively detect high-impact variables during the sequential modelling procedure.

To demonstrate our method, we conduct several sequential fixed-sized confidence set estimation procedures at once on different machines (or central processing units (CPUs)); then, once all the ‘sampling’ procedures are stopped, we combine their estimates. This kind of computation scheme is similar to ‘distributed’ computing 