introducing biases, as well as partition sizes, there has been a lack of discussion in the literature regarding how to legitimately integrate all the results from each partition into a statistically meaningful estimate. This will be an important issue when there is inconsistency among the results derived from partitioning. One of the advantages inherent in using a sequential methodology is that we do not need to specify the sample size first. For this reason, we can conduct $M$ sequential fixed-width confidence set estimation procedures on these machines. With the properties of the fixed-sized confidence set estimation procedure, the estimates of various sequential estimation procedures will have a similar estimation accuracy and coverage probabilities; this will allow us to combine them into an estimate while retaining the desired statistical properties.

Sequential fixed-sized confidence set estimate for regression model
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Our present goal is to estimate regression parameters and build with them a confidence set of a prescribed size. We independently run $M$ estimation procedures; each of them will independently and sequentially recruit observations from the same data set, without replacement. When all sequential procedures are stopped, we combine their results into one estimate, such that it can retain the desired statistical 