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Search and optimal sample sizes
 Review of Economic Studies
, 1983
"... This paper considers the wide class of problems in which a searcher can choose his sample size and whether or not to stop search at each of a sequence of decision points. Sequential search problems are the special cases in which the sample size chosen at each decision point is unity. Several propert ..."
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Cited by 13 (0 self)
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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
Optimal Sample Size Allocation for Accelerated Degradation Test
"... Accelerated Degradation tests (ADTs) are widely used to assess the lifetime information of highly reliable products possessing quality characteristics that both degrade over time and can be related to reliability. Hence, how to design an efficient ADT plan for assessing product’s lifetime informatio ..."
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information at normaluse stress (especially for the optimal samplesize allocation to higher teststress 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
Detecting the optimal sample size in adaptive designs
"... Abstract: The adaptive cluster sampling (ACS) is a suitable sampling design for populations which are rare and have cluster tendencies. In environmental and ecological applications, biological populations are generally animals or plants with spatial distribution highly patchy. ACS has the drawback t ..."
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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 datadriven procedure in order to determine when to stop
Optimal Sample Size for Multiple Testing: the Case of Gene Expression Microarrays
 Journal of the American Statistical Association
, 2004
"... 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 multip ..."
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Cited by 75 (5 self)
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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
CLUSTER OPTIMAL SAMPLE SIZE FOR DEMOGRAPHIC AND HEALTH SURVEYS
 ICOTS7, 2006: ALIAGA AND REN
, 2006
"... For practical purposes and simplicity, the sample design used in the Demographic and Health surveys is a twostages clustered sample. In general, the sampling frame is a complete list of enumeration areas (EAs) created in a recent population census (around 100 households per EA). In a second stage, ..."
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, a prefixed number of households is selected from each EA. All household members (all women ages1549 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
THE DETERMINATION OF THE OPTIMAL SAMPLE SIZE FOR RELIABILITY SCALES IN SOCIAL SCIENCES
"... In many areas of research, the precise measurement of hypothesized processes or variables (theoretical constructs, latent variables) poses a challenge by itself. In social sciences, unreliable measurements of people’s beliefs or intentions obviously hamper efforts to predict their behavior. Each mea ..."
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sample size to estimate the probability that Cronbach’s alpha exceeds a prespecified desirable level of accuracy. The Bayesian point of view is employed. 1.
Asymptotic Optimal Sample Sizes for Discovering a Promising Treatment in Phase II Group Sequential Clinical Trials
"... We propose an asymptotic optimal procedure for determining the perstudy 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. Ou ..."
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We propose an asymptotic optimal procedure for determining the perstudy 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
Outofbag Estimation of the Optimal Sample Size in Bagging
, 2009
"... The performance of moutofn bagging with and without replacement in terms of the sampling ratio (m/n) is analyzed. Standard bagging uses resampling with replacement to generate bootstrap samples of equal size as the original training set mwor = n. Withoutreplacement methods typically use half sam ..."
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Cited by 4 (0 self)
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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 outofbag estimates of the generalization accuracy to select a nearoptimal value for the sampling ratio. Ensembles of classifiers
Determining the optimal sample size in the Monte Carlo experiments 1
"... 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 ou ..."
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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
DEBIASING THE LASSO: OPTIMAL SAMPLE SIZE FOR GAUSSIAN DESIGNS By
, 2015
"... Performing statistical inference in highdimensional models is an outstanding challenge. A major source of difficulty is the absence of precise information on the distribution of highdimensional regularized estimators. Here, we consider linear regression in the highdimensional regime p n and the ..."
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Performing statistical inference in highdimensional models is an outstanding challenge. A major source of difficulty is the absence of precise information on the distribution of highdimensional regularized estimators. Here, we consider linear regression in the highdimensional regime p n and the Lasso estimator. In this context, we would like to perform inference on a highdimensional parameters vector θ ∗ ∈ Rp. Important progress has been achieved in computing confidence intervals and pvalues for single coordinates θ∗i, i ∈ {1,..., p}. A key role in these new inferential methods is played by a certain debiased (or desparsified) 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 s0sparse with s0 = o( n / log p). The condition s0 = o( n / log p) is considerably stronger than the one required for consistent
Results 1  10
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82,190