Considering a positive portfolio diffusion X with negative drift, we investigate optimal stopping problems of the form inf θ E f  Xθ sup s∈[0,τ ] Xs   , where f is a non-increasing function, τ is the next random time where the portfolio X crosses zero and θ is any stopping time smaller than τ . Hereby, our motivation is the obtention of an optimal selling strategy minimizing the relativ...

In this work we apply the FDA proposed precision criteria necessary for pediatric pharmacokinetic studies (Wang et. al., 2012) as a stopping criteria for a model based adaptive optimal design (MBAOD) of an adult to children pharmacokinetic bridging study. We demonstrate the power of the MBAOD compared to both traditional designs as well as non-adaptive optimal designs.

Over 50% of patients are already taking blood pressure-lowering therapy on hospital admission for acute stroke. An individual patient data meta-analysis from randomized controlled trials was undertaken to determine the effect of continuation versus temporarily stopping preexisting antihypertensive medication in acute stroke. Key databases were searched for trials against the following inclusion...

This paper defines and studies a variable selection procedure called Testing-Based Forward Model Selection. The procedure inductively selects covariates which increase predictive accuracy into a working statistical regression model until a stopping criterion is met. The stopping criteria and selection criteria are defined using statistical hypothesis tests. The paper explicitly describes a test...

Schematic networks are linear abstractions of functional networks, such as route networks. Lines in the original network are modified in order to produce a schematic network which satisfies a set of constraints chosen to design the network. A method is described which accomplishes this line transformation using an iterative improvement technique driven by design constraints. The method maintain...

Sequential optimality conditions for constrained optimization are necessarily satisfied by local minimizers, independently of the fulfillment of constraint qualifications. These conditions support the employment of different stopping criteria for practical optimization algorithms. On the other hand, when an appropriate strict constraint qualification associated with some sequential optimality c...

We derive a posteriori error estimates for the discontinuous Galerkin method applied to the Poisson equation. We allow for a variable polynomial degree and simplicial meshes with hanging nodes and propose an approach allowing for simple (nonconforming) flux reconstructions in such a setting. We take into account the algebraic error stemming from the inexact solution of the associated linear sys...

It is well known that the boosting-like algorithms, such as AdaBoost and many of its modifications, may over-fit the training data when the number of boosting iteration becomes large. Therefore, how to stop a boosting algorithm at an appropriate iteration time is a longstanding problem for the past decade (see Meir and Rastch (2003)). Bühlmann and Yu (2005) apply model selection criteria to est...

When combining a set of learned models to form an improved estimator, the issue of redundancy in the set of models must be addressed. Existing methods for addressing this problem have failed to perform robustly, especially as the redundancy in the set of learned models increases. Recently, a variant of principal components regression, PCR*, demonstrated that these limitations could be overcome ...