Accelerated group sequential sampling

Aim/purpose – This work aims to assess stochastic properties of finite population mean estimators under Pathak’s fixed-cost sequential sampling scheme by deriving their co- variance and proposing its estimator. This scheme enables strict control over survey- -related monetary expenditures when individual data acquisition costs differ and survey budget excesses are unacceptable. It facilitates economically efficient use of available resources. Our work is aimed at extending its applicability. Design/methodology/approach – This paper’s main theoretical result concerning the covariance of Pathak mean value estimators and the unbiasedness of its proposed estima- tor was derived analytically. A short simulation study supports this. Findings – The proposed estimator for the covariance between Pathak’s estimators of population averages is unbiased under the sequential fixed-cost sampling scheme, which prevents survey budget excesses when the per-unit costs of acquiring information are known in advance but not homogeneous. Research implications/limitations – Our findings facilitate accuracy assessment for complex estimators of population parameters that utilize auxiliary information under restricted-budget sequential sampling and, in particular, for ratio, product, and regression estimators. They are potentially applicable to any quantities of interest that could be expressed in terms of averages, including, among others, unemployment rates, poverty measures, inflation indices, and GDP indicators. Their application is justified if the per- unit costs of acquiring information vary. Originality/value/contribution – Our results allowed us to assess various economic variables’ distribution characteristics while controlling for randomly varying survey costs. This facilitates planning and managing sample surveys in an economically effi- cient way. Keywords: Pathak’s estimator, sequential sampling, fixed-cost sampling, sum-quota sampling. JEL Classification C15, C83.

This work presents a computationally efficient probabilistic framework that enables the identification of model parameters from noisy measurements of the response. We consider transient PDE-based models, where the parameters correspond to physical properties. An efficient and reliable procedure for estimation of those unknown parameters is pursued. The proposed framework uses a Bayesian approach, an efficient sequential Monte Carlo sampling scheme, and adaptive reduced-order models (ROMs). The Bayesian approach has several advantages including the ability to provide not only point estimates of the quantities of interest, but also measures of credibility and robustness concerning those estimates. The associated sequential Monte Carlo method sampling scheme is embarrassingly parallelizable, as well as efficient in terms of the number of calls to the forward solver (e.g. a finite element code) used in evaluating the likelihood function. We propose to use a ROM adaptation procedure where projection-based ROMs are seen as points on a certain Riemannian manifold and are ‘tracked’ and interpolated during the sampling process using a database of precomputed ROMs. This approach ensures that an appropriate ROM is used for the likelihood evaluation in every region of the parameter space, thus leading to computational savings while conserving sufficient accuracy. Using numerical examples, we illustrate the capabilities of the proposed framework, and show that it leads to quality estimates with a quantified predictive uncertainty.

Investigating adaptive sampling strategies for optimal building energy performance using artificial neural networks and kriging surrogate models

The initiation of post-diapause development in Blissus leucopterus hirtus was marked by oogenesis in overwintered females. Its temperature threshold was established as 7°C. Sequential sampling and chemical applications were timed from a knowledge of day degrees accumulated above this threshold to the peak in numbers of 3rd-instar nymphs. The sequential sampling scheme was developed to identify injurious infestations of B. l. hirtus and to recommend or advise against chemical treatment. It was based on the size of the population of 3rd-instar nymphs, sampled at their peak in relation to 2 critical densities. This stage of the insect conforms to a negative binomial distribution with a common k of 0.401. The system proved effective, both in timing and accuracy of prediction, when put into practice on 33 lawns in Guelph, Ontario.

Ratio and regression estimators for a mean are considered in conjunction with certain sequential sampling schemes. An auxiliary variable is assumed present and both fixed-cost and fixed- width confidence interval stopping rules are investigated. The asymptotic distributions of the estimators are derived as well as optimal probabilities pertinent to the schemes. Comparisons are made with results of certain double sampling procedures. Estimation of the ratio of two means is also considered and the results of a Monte Carlo simulation are included.

Given a observations from each of k populations whose distributions differ by a location parametar,the value of the largest parameter is to be estimated using the largest value of the k sample means. It is desired to design, a sampting rule which guarantees that the Mean Squarad Error (M.S.E.) of the estimate does not exceed a given bound when the distributions have a common but unknown scale parameter. A sequential sampling scheme is devised based on an estimate of the scale parameter and a “least favorable” configuration of the location parameters. The sample size characteristics of the sampling plan are studied under mild restrictions on the distributions involved. The M.S.E. of the resulting estimator is studied under the additional assumption of normality. A brief discussion is given of an alternate sequential plan which uses informationin the sample regarding the configuracion of the Locaeion parameters.

ABSTRACTAbout 700,000 Accredited Social Health Activists (ASHA) have been deployed as community health volunteers throughout India over the last few years. The objective of our study was to assess adherence to selection criteria in the recruitment of ASHA workers and to assess their performance against their job descriptions in Karnataka state, India. A cross-sectional survey, using a combination of quantitative and qualitative methods, was undertaken in 2012. Three districts, 12 taluks (subdistricts), and 300 villages were selected through a sequential sampling scheme. For the quantitative survey, 300 ASHAs and 1,800 mothers were interviewed using sets of structured questionnaire. For the qualitative study, programme officers were interviewed via in-depth interviews and focus group discussions. Mean±SD age of ASHAs was 30.3±5.0 years, and about 90% (261/294) were currently married, with eight years of schooling. ASHAs were predominantly (>80%) involved in certain tasks: home-visits, antenatal counselling, delivery escort services, breastfeeding advice, and immunization advice. Performance was moderate (40-60%) for: drug provision for tuberculosis, caring of children with diarrhoea or pneumonia, and organizing village meetings for health action. Performance was low (<25%) for advice on: contraceptive-use, obstetric danger sign assessment, and neonatal care. This was self-reported by ASHAs and corroborated by mothers. In conclusion, ASHA workers were largely recruited as per preset selection criteria with regard to age, education, family status, income, and residence. The ASHA workers were found to be functional in some areas with scope for improvement in others. The role of an ASHA worker was perceived to be more of a link-worker/facilitator rather than a community health worker or a social activist.

A sequential sparse polynomial chaos expansion using Voronoi exploration and local linear approximation exploitation for slope reliability analysis

A sequential sampling scheme is in operation to test the viability and wood decaying ability of commercially produced sachets of Peniophora giganitea suspension in the treatment of pine stumps against Heterobasidion annosum.

We consider efficient study designs to estimate sensitivity and specificity of a candidate diagnostic or screening test. Our focus is the setting in which the candidate test is inexpensive to administer compared to evaluation of disease status, and the test results, available in a large cohort, can be used as a basis for sampling subjects for verification of disease status. We examine designs in which disease status is verified in a sample chosen so as to optimize estimation of either sensitivity or specificity. We then propose a sequential design in which the first step of sampling is conducted to efficiently estimate specificity. If the candidate test is determined to be of sufficient specificity, then step two of sampling is conducted to estimate sensitivity. We propose estimators based on this sequential sampling scheme, and show that the performance of these estimators is excellent. We develop sample size calculations for the sequential design, and show that this design, in most situations, compares favourably in terms of expected sample size to a fixed size design.

Minimally sufficient numbers of measurements for validation of 24-hour blood pressure monitoring in chronic kidney disease

The Rasch model allows for a conditional likelihood ratio goodness of fit test. The speed of approximation of the test statistic to the limiting distribution as a function of sample size and test length has not been analyzed so far. Three bootstrap simulation methods are analyzed with respect to their performance in providing a proper distribution of the test statistic under the null- and the alternative hypothesis. We found a stable approximation to the limiting χ 2-distribution for sample sizes of at least 500 and 10 items. The three bootstrap algorithms rendered consistent results for the H 0-cases but not for the H 1-cases. A sequential probability sampling scheme proves sufficiently apt for generating samples under the alternative hypothesis. This superiority can be justified from a theoretical point of view.

Several model validation and prediction error modeling techniques are studied and compared in this paper to help establish stopping criteria and identify critical regions in the design space in a sequential sampling framework. This study leads to the proposal of a two-phase sequential sampling and meta-modeling strategy, which is realized by the support of a multi-dimensional data visualization tool. These techniques have been successfully applied in the development and setup of a system-level parametric tool to support Heating, Ventilating, and Air Conditioning design. Maintaining the same level of accuracy, we observe a savings of 6–30 times the simulation effort needed for current practice. The benefits and drawbacks of the method are discussed, and opportunities are identified for future improvement.

Surrogate Models (SM) are now regarded as powerful tools for engineering design applications; they approximate expensive responses obtained by either a computer simulation or a real experiment with more computationally efficient mathematical models. The goal of reducing the number of function evaluations required to create the training dataset led to the development of strategies capable of effectively sampling the design space. In particular, sequential adaptive techniques seem to be the most promising approaches for engineering design applications. Unfortunately, most of these techniques supervise the sampling phase with an SM created from the available training set, making their behavior dependent on the chosen SM formulation. This characteristic can cause sampling performance deterioration, in particular when an inaccurate SM formulation is used to supervise the sampling process. The solution proposed in this paper tries to mitigate this problem by coupling the model-dependent sequential adaptive technique with an active SM selection approach. This surrogate modeling architecture is tested on several example functions, and results indicate that it inherits the advantages of both constituent elements: an increased flexibility due to the active SM selection and a reduced total number of samples due to the sequential adaptive sampling strategy. Other parameters which may affect the sampling performance (such as the SM selection frequency and criterion) are also analyzed in an attempt to define general guidelines for the application of the method. The sampling technique considered in this study is based on cross-validation variance, and the surrogate modeling performance is assessed on nine two-dimensional and two five-dimensional test functions.

Sequential sampling schemes for testing a paddock for infestation by grass grub (Costelytra zealandica (White) (Coleoptera: Scarabaeidae)) larvae are developed. Instead of taking a fixed number of samples per paddock, decisions are made during sampling as to whether sampling needs to be continued. The hypothesis that the larvae have a negative binomial distribution is tested and accepted. The performance of the schemes is assessed theoretically for larvae populations with varying degrees of clumping. Their performance in practice is compared with that of schemes which use a fixed number of samples, and it is found that the sequential schemes significantly reduce the number of samples required with little loss of accuracy.