essential one. Computer scientists may want to resolve these issues from algorithm and hardware perspectives, but these usually require a complicated software setup and/or modern computation facilities. In the current study, we adopt sequential estimation methods for regression models and allow the model for each ‘partition’ to sequentially choose from the data pool its own new subjects, until its stopping criterion is fulfilled. With a suitable selected stopping criterion, we are able to combine and integrate the models and maintain good statistical properties in such an integrated estimate. Conventionally, how to partition a large data set into several small ones may affect the overall analytical performance. Here, the data sizes of the individual partitions differ and depend on the corresponding performance of the model of each partition. In particular, we adopt the method of the fixed-size confidence set estimation [@Siegmund:1985], such that when data recruiting is stopped, the estimates will have a prescribed accuracy. Because the accuracy of the coefficient estimates from each partition is under control, we are able to merge these estimates into one and retain the required statistical properties of the original sequential estimation.

The method of sequential analysis was established by [@ward1945; @ward1947], and it has 