properties of the optimal sample size sequence are established, with particular attention being paid to the effects of recall, decision horizons and fallback utilities. These properties yield necessary and sufficient conditions for the optimality of sequential search strategies within the class of problems

information at normal-use stress (especially for the optimal sample-size allocation to higher test-stress levels) turns out to be a challenging issue for reliability analysts. In the literature, several papers had addressed this decision problem. However, the results are only based on a specific degradation

that the final sampling fraction may be very large and uncertain. In this paper we propose a two stage adaptive cluster sampling in which a stopping rule is introduced in order to limit sampling. The idea is to detect the optimal sample size by means of a data-driven procedure in order to determine when to stop

We consider the choice of an optimal sample size for multiple comparison problems. The motivating application is the choice of the number of microarray experiments to be carried out when learning about dierential gene expression. However, the approach is valid in any application that involves

, a prefixed number of households is selected from each EA. All household members (all women ages15-49 in particular) are selected for interviewing. This presentation looks at nearly optimum sample sizes and compares them with different situations. The results show that for most of the Demographic

sample size to estimate the probability that Cronbach’s alpha exceeds a pre-specified desirable level of accuracy. The Bayesian point of view is employed. 1.

We propose an asymptotic optimal procedure for determining the per-study sample size of a series of Phase II clinical studies. The sequence of studies seeks to discover an active treatment among a sequence of treatments under evaluation. Each phase II study proceeds in r stages with r=1, 2, or 3

samples mwr = n/2. These choices of sampling sizes are arbitrary and need not be optimal in terms of the classification performance of the ensemble. We propose to use the out-of-bag estimates of the generalization accuracy to select a near-optimal value for the sampling ratio. Ensembles of classifiers

Abstract A convergence criterion for the Monte Carlo estimates will be proposed which can be used as a stopping rule for the Monte Carlo experiments. The proposed criterion searches a convergence band of a given width and length such that the probability of the Monte Carlo sample variance to fall

Performing statistical inference in high-dimensional models is an outstanding challenge. A ma-jor source of difficulty is the absence of precise information on the distribution of high-dimensional regularized estimators. Here, we consider linear regression in the high-dimensional regime p n and the Lasso estimator. In this context, we would like to perform inference on a high-dimensional parameters vector θ ∗ ∈ Rp. Important progress has been achieved in computing confidence intervals and p-values for single coordinates θ∗i, i ∈ {1,..., p}. A key role in these new inferential methods is played by a certain de-biased (or de-sparsified) estimator θ̂d that is constructed from the Lasso estimator. Earlier work establishes that, under suitable assumptions on the design matrix, the coordinates of θ̂d are asymptotically Gaussian provided the true parameters vector θ ∗ is s0-sparse with s0 = o( n / log p). The condition s0 = o( n / log p) is considerably stronger than the one required for consistent