Non-response is a problem for most surveys. In the sample design, non-response is often dealt with by setting a target response rate and inflating the sample size so that the desired number of interviews is reached. The decision to stop data collection is based largely on meeting the target response rate. A recent article by Rao, Glickman, and Glynn (RGG) suggests rules for stopping that are ba...

Consider a finite irreducible Markov chain with transition matrix M = (pij). Fixing a target distribution τ , we study a family of optimal stopping rules from the singleton distributions to τ . We show that this family of rules is dual to a family of (not necessarily optimal) rules on the reverse chain from the singleton distributions to a related distribution α̂ called the τ -contrast distribut...

This paper argues that software testing can be less thorough yet more efficient if applied in a well-managed, empirical manner across the entire Software Development Life Cycle (SDLC). To ensure success, testing must be planned and executed within an Earned Value Management (EVM) paradigm. A specific example of empirical software testing is given: the Empirical Bayesian Stopping Rule (EBSR). Th...

Testing behavioral models before they are released to the synthesis and logic design phase is a tedious process, to say the least. A common practice is the test-it-to-death approach in which millions or even billions of vectors are applied and the results are checked for possible bugs. The vectors applied to behavioral models include functional vectors, but the significant amount of the vectors...

Clinical trial designs often incorporate a sequential stopping rule to serve as a guide in the early termination of a study. When choosing a particular stopping rule, it is most common to examine frequentist operating characteristics such as type I error, statistical power, and precision of confidence intervals (Statist. Med. 2005, in revision). Increasingly, however, clinical trials are design...

This paper studies the regularization of constrained Maximum Likelihood iterative algorithms applied to incompatible ill-posed linear inverse problems. Specifically we introduce a novel stopping rule which defines a regularization algorithm for the Iterative Space Reconstruction Algorithm in the case of Least-Squares minimization. Further we show that the same rule regularizes the Expectation M...

This article studies an optimal stopping problem with an endogenous constraint on the set of admissible stopping times. The constraint stipulates that continuation is permitted, at any given date t, only if the endogenous reward achieved exceeds a prespecified threshold. Characterizations of the value function and the optimal stopping time are presented. An application to the pricing of corpora...

In the house-hunting problem, i.i.d. random variables, X1,X2, . . . are observed sequentially at a cost of c > 0 per observation. The problem is to choose a stopping rule, N , to maximize E(XN − Nc). If the X’s have a finite second moment, the optimal stopping rule is N∗ = min{n ≥ 1 : Xn > V ∗}, where V ∗ satisfies E(X − V ∗)+ = c. The statement of the problem and its solution requires only the...

The paper deals with an optimal stopping problem with a finite planning horizon where an available search budget, the total amount of money that can be invested in search activities throughout the planning horizon, is limited and where both the probability of an offer being obtained at each point in time and the probability distribution function of an obtained offer's value may depend on the se...

equivalent to saying that states can be represented by points in some euclidean space so that the distance between i and j is p pij.) It is virtually never possible to nd a blind rule for generating which is mean-optimal. If we consider only stopping at some xed time, even approximating may take far more time than a mean-optimal rule (for example when the chain is almost periodic). However, the...