Nonanticipating estimation applied to sequential analysis and changepoint detection

Nonanticipating Estimation Applied to Sequential Analysis and Changepoint Detection

Suppose a process yields independent observations whose distributions belong to a family parameterized by θ ∈Θ. When the process is in control, the observations are i.i.d. with a known parameter value θ0. When the process is out of control, the parameter changes. We apply an idea of Robbins and Siegmund [Proc. Sixth Berkeley Symp. Math. Statist. Probab. 4 (1972) 37–41] to construct a class of s...

Sequential Analysis : Hypothesis Testing and Changepoint Detection

Sequential Analysis: Hypothesis Testing and Changepoint Detection Report Title The main focus of this book is on a systematic development of the theory of sequential hypothesis testing (Part I) and changepoint detection (Part II). In Part III, we briefly describe certain important applications where theoretical results can be used efficiently, perhaps with some reasonable modifications. We revi...

Temporal difference learning applied to sequential detection

This paper proposes a novel neural-network method for sequential detection, We first examine the optimal parametric sequential probability ratio test (SPRT) and make a simple equivalent transformation of the SPRT that makes it suitable for neural-network architectures. We then discuss how neural networks can learn the SPRT decision functions from observation data and labels. Conventional superv...

Sequential Bayesian Estimation And Model Selection Applied To Neural Networks

Sequential Joint Detection and Estimation ∗

We consider the problem of simultaneous detection and estimation under a sequential framework. In particular, we are interested in sequential tests that distinguish between the null and the alternative hypothesis, and every time the decision is in favor of the alternative they provide an estimate of a random parameter. As we demonstrate with our analysis, treating the two subproblems separately...