Biological research often involves testing a growing number of null hypotheses as new data are accumulated over time. We study the problem of online control of the familywise error rate, that is testing an a priori unbounded sequence of hypotheses ( pvalues) one by one over time without knowing the future, such that with high probability there are no false discoveries in the entire sequence. This paper unifies algorithmic concepts developed for offline (single batch) familywise error rate control and online false discovery rate control to develop novel online familywise error rate control methods. Though many offline familywise error ratemore »
THE EASE OF FITTING BUT FUTILITY OF TESTING A NONSTATIONARY POISSON PROCESSES FROM ONE SAMPLE PATH
The nonstationary Poisson process (NSPP) is a workhorse tool for modeling and simulating arrival processes
with timedependent rates. In many applications only a single sequence of arrival times are observed. While
one sample path is sufficient for estimating the arrival rate or integrated rate function of the process—as
we illustrate in this paper—we show that testing for Poissonness, in the general case, is futile. In other
words, when only a single sequence of arrival data are observed then one can fit an NSPP to it, but the
choice of “NSPP” can only be justified by an understanding of the underlying process physics, or a leap
of faith, not by testing the data. This result suggests the need for sensitivity analysis when such a model
is used to generate arrivals in a simulation.
 Editors:
 Bae, KH; Feng, B; Kim, S; LazarovaMolnar, S; Zheng, Z; Roeder, T; Thiesing, R
 Award ID(s):
 1854562
 Publication Date:
 NSFPAR ID:
 10233327
 Journal Name:
 Proceedings of the Winter Simulation Conference
 Page Range or eLocationID:
 266276
 ISSN:
 15584305
 Sponsoring Org:
 National Science Foundation
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