Michael Baron

Learn More
In the early 1960s, Shiryaev obtained the structure of Bayesian stopping rules for detecting abrupt changes in independent and identically distributed sequences as well as in a constant drift of the Brownian motion. Since then, the methodology of optimal change-point detection has concentrated on the search for stopping rules that achieve the best balance(More)
The shells of most lacustrine gastropods are typically small, weakly calcified, and modestly ornamented to unornamented. Similarly, most lacustrine crabs are usually small detritivores with weak chelae. A number of invertebrate taxa in Lake Tanganyika, however, deviate from these generalities. This study explores a predator-prey coevolution model as an(More)
Sequential procedures are developed for simultaneous testing of multiple hypotheses in sequential experiments. Proposed stopping rules and decision rules achieve strong control of both familywise error rates I and II. The optimal procedure is sought that minimizes the expected sample size under these constraints. Bonferroni methods for multiple comparisons(More)
Classifying instances in evolving data stream is a challenging task because of its properties, e.g., infinite length, concept drift, and concept evolution. Most of the currently available approaches to classify stream data instances divide the stream data into fixed size chunks to fit the data in memory and process the fixed size chunk one after another.(More)
Sequential methods are developed for testing multiple hypotheses, resulting in a statistical decision for each individual test and controlling the familywise error rate and the familywise power in the strong sense. Extending the ideas of stepup and step-down methods for multiple comparisons to sequential designs, the new techniques improve over the(More)
The application of the CDM model has proven to be effective and accurate. The piecewise approximation used relies on the proper identification of re-calibration points. These points are identified based on the observation of the testing process. However, not all change points are easily identifiable. In this paper, we present a new technique for the(More)
To decide if an update to a data stream classifier is necessary, existing sliding window based techniques monitor classifier performance on recent instances. If there is a significant change in classifier performance, these approaches determine a chunk boundary, and update the classifier. However, monitoring classifier performance is costly due to scarcity(More)