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Sequential Estimation: Ghosh/Sequential

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The article was published on 1997-02-26. It has received 231 citations till now. The article focuses on the topics: Sequential estimation.

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Dose finding using the biased coin up-and-down design and isotonic regression.

TL;DR: A linearly interpolated isotonic regression estimate is shown to be simple to derive and to perform as well as or better than the other target dose estimators in terms of mean square error and average number of subjects needed for convergence in most scenarios studied.
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A Sequential Sampling Procedure for Stochastic Programming

TL;DR: A sequential sampling procedure for a class of stochastic programs that estimates the optimality gap of a candidate solution from a sequence of feasible solutions generated by solving a series of sampling problems with increasing sample size.
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Two-Stage Procedures for High-Dimensional Data

TL;DR: In this paper, the sample size determination for high-dimensional data was proposed by using new types of multivariate two-stage estimation methodologies, and the most important and basic idea is the asymptotic normality when p ≤ ∞.
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ASTRO-DF: A Class of Adaptive Sampling Trust-Region Algorithms for Derivative-Free Stochastic Optimization

TL;DR: In this article, the authors consider unconstrained optimization problems where only stochastic estimates of the objective function are observable as replicates from a Monte Carlo oracle, and assume that the oracle is known.
Journal ArticleDOI

Asymptotic Optimality of Change-Point Detection Schemes in General Continuous-Time Models

TL;DR: In this article, the authors investigated the asymptotic performance of the Shiryaev Bayesian change detection procedure for general continuous-time stochastic models for a small false alarm probability.