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...

An approximation tothe sequential updating of the distribution flocation parameters of a linear time series model is developed for non-normal observations. The behaviour of the resulting non-linear recursive filtering algorithm isexamined and shown to have certain desirable properties for a variety of non-normal error distributions. Illustrative examples are given and relationships with previou...

By the sufficiency principle, the probability density of a sequential test statistic under certain conditions can be factored into a known function that does not depend on the stopping rule and a conditional probability that is free of unknown parameters. We develop general theorems and propose a unified approach to analyzing and evaluating various properties of sequential tests and post-test e...

Recently developed methods for power estimation have primarily focused on combinational logic. In this paper, we present a framework for the eecient and accurate estimation of average power dissipation in sequential circuits. Switching activity is the primary cause of power dissipation in CMOS circuits. Accurate switching activity estimation for sequential circuits is considerably more diicult ...

A new method for power estimation in sequential circuits is presented that is based on a statistical estimation technique. By applying randomly generated input sequences to the circuit, statistics on the latch outputs are collected, by simulation, that allow e cient power estimation for the whole design. An important advantage of this approach is that the desired accuracy can be speci ed up-fro...

In recent works, the first author has developed a constraint estimation technique that can be applied to the kinematic tracking problem. This a posteriori technique maintains an unconstrained estimate based solely on the measurements while the constrained estimate is computed, if necessary, at each time step. This technique has been expanded to incorporate nonlinear systems and time-varying sys...

There is considerable interest in methods that use accumulated data to modify trial sample size. However, sample size re-estimation in group sequential designs has been controversial. We describe a method for sample size re-estimation at the penultimate stage of a group sequential design that achieves specified power against an alternative hypothesis corresponding to the current point estimate ...

We propose three efficient sequential designs in the software reliability estimation. The fully sequential design the multistage sequential design and the accelerated sequential design. These designs make allocation decisions dynamically throughout the testing process. We then refine these estimated reliabilities in an iterative manner as we sample. Monte Carlo simulation seems to indicate that...

Bayesian estimation of DSGE models typically uses Markov chain Monte Carlo as importance sampling (IS) algorithms have a difficult time in high-dimensional spaces. I develop improved IS algorithms for DSGE models using recent advances in Monte Carlo methods known as sequential Monte Carlo samplers. Sequential Monte Carlo samplers are a generalization of particle filtering designed for full simu...