scholarly journals Multi-species attributes as the condition for adaptive sampling of rare species using two-stage sequential sampling with an auxiliary variable

2011 ◽  
Keyword(s):  
Rare Species ◽  
Adaptive Sampling ◽  
Sequential Sampling ◽  
Auxiliary Variable ◽  
Two Stage

scholarly journals Two-stage sequential sampling for two rare species in western Inner Mongolia of China

2012 ◽  
Vol 58 (4-5) ◽  
pp. 507-516
Author(s):  
JingJing Shi ◽  
TianZhong Zhao ◽  
YuanCai Lei
Keyword(s):  
Rare Species ◽  
Inner Mongolia ◽  
Sequential Sampling ◽  
Two Stage

scholarly journals ASAMS: An Adaptive Sequential Sampling and Automatic Model Selection for Artificial Intelligence Surrogate Modeling

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5332
Author(s):  
Carlos A. Duchanoy ◽  
Hiram Calvo ◽  
Marco A. Moreno-Armendáriz
Keyword(s):  
Artificial Intelligence ◽  
Model Selection ◽  
Adaptive Sampling ◽  
Search Algorithm ◽  
Sequential Sampling ◽  
Surrogate Modeling ◽  
Kriging Model ◽  
Surrogate Models ◽  
Physical Experiments ◽  
Leave One Out

Surrogate Modeling (SM) is often used to reduce the computational burden of time-consuming system simulations. However, continuous advances in Artificial Intelligence (AI) and the spread of embedded sensors have led to the creation of Digital Twins (DT), Design Mining (DM), and Soft Sensors (SS). These methodologies represent a new challenge for the generation of surrogate models since they require the implementation of elaborated artificial intelligence algorithms and minimize the number of physical experiments measured. To reduce the assessment of a physical system, several existing adaptive sequential sampling methodologies have been developed; however, they are limited in most part to the Kriging models and Kriging-model-based Monte Carlo Simulation. In this paper, we integrate a distinct adaptive sampling methodology to an automated machine learning methodology (AutoML) to help in the process of model selection while minimizing the system evaluation and maximizing the system performance for surrogate models based on artificial intelligence algorithms. In each iteration, this framework uses a grid search algorithm to determine the best candidate models and perform a leave-one-out cross-validation to calculate the performance of each sampled point. A Voronoi diagram is applied to partition the sampling region into some local cells, and the Voronoi vertexes are considered as new candidate points. The performance of the sample points is used to estimate the accuracy of the model for a set of candidate points to select those that will improve more the model’s accuracy. Then, the number of candidate models is reduced. Finally, the performance of the framework is tested using two examples to demonstrate the applicability of the proposed method.

Adaptive sampling

2019 ◽  
pp. 219-239
Author(s):  
David G. Hankin ◽  
Michael S. Mohr ◽  
Ken B. Newman
Keyword(s):  
Rare Species ◽  
Adaptive Sampling ◽  
Sampling Procedure ◽  
Cluster Sampling ◽  
Single Individual ◽  
Adaptive Cluster Sampling ◽  
Sampling Strategies ◽  
Good Sense ◽  
Total Cost ◽  
Selection Of

The abundance of rare species of plants and animals may often prove difficult to estimate due to the isolated patchy distribution of individuals. Adaptive sampling may prove more effective than other sampling strategies for such species. In adaptive cluster sampling an initial SRS of population units is selected. Further adaptive sampling in the neighborhood of these units is then carried out whenever the value of y in a selected unit meets or exceeds a criterion value, c, which may often be just a single individual. This sampling procedure can be shown to lead to selection of clusters of units for which, with the exception of edge units, all units in the selected clusters have y≥c. If the initial sample is large enough to encounter some isolated patches of individuals, this approach may outperform SRS with mean-per-unit estimation. Drawbacks of this approach include the facts that the eventual number of population units which will need to be measured is random and unknown prior to execution of the survey, and it is difficult to specify the magnitude of the adaptive sampling criterion, c. Therefore, the total cost and time needed to complete an adaptive sampling survey can be highly unpredictable. Nevertheless, the theory is intriguing and has obvious intuitive appeal. Once a very rare individual has been encountered, it makes good sense to search very carefully in the neighborhood of the location where that rare individual has been found.

A Comparison of Two-Stage Scheme and Fully Sequential Sampling Scheme for Estimating Software Reliability

2020 ◽  
Vol 10 (10) ◽  
pp. 1-5
Author(s):  
Bader S Alanazi
Keyword(s):  
Software Reliability ◽  
Sequential Sampling ◽  
Sampling Scheme ◽  
Test Cases ◽  
Two Stage ◽  
Balanced Sampling ◽  
Stage Scheme ◽  
Estimate Reliability ◽  
Two Phases ◽  
Better Than

In this paper, we compare two-stage sequential sampling scheme with fully sequential sampling scheme to test software and estimate reliability. In two-stage sampling scheme, test cases can be allocated among partitions in two phases. Our goal of this scheme is to obtain the near-optimal choices for distributing of test cases among sub-domains by minimizing the variance of the overall software reliability estimator. The two-stage sampling scheme is expected to be more convenient than a fully sequential sampling scheme because it requires fewer computations than the fully sequential sampling scheme. Also, the two-stage sampling scheme is expected to perform better than a balanced sampling scheme by virtue of lower the variance incurred by the overall estimated software reliability

scholarly journals Two-stage sampling in the estimation of growth parameters and percentile norms: sample weights versus auxiliary variable estimation

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
George Vamvakas ◽  
Courtenay Norbury ◽  
Andrew Pickles
Keyword(s):  
Parameter Estimation ◽  
Structural Equation ◽  
Growth Parameters ◽  
Growth Models ◽  
Auxiliary Information ◽  
Auxiliary Variable ◽  
Auxiliary Variables ◽  
Growth Data ◽  
Sampling Weights ◽  
Two Stage

Abstract Background The use of auxiliary variables with maximum likelihood parameter estimation for surveys that miss data by design is not a widespread approach, despite its documented improved efficiency over traditional approaches that deploy sampling weights. Although efficiency gains from the use of Normally distributed auxiliary variables in a model have been recorded in the literature, little is known about the effects of non-Normal auxiliary variables in the parameter estimation. Methods We simulate growth data to mimic SCALES, a two-stage survey of language development with a screening phase (stage one) for which data are observed for the whole sample and an intensive assessments phase (stage two), for which data are observed for a sub-sample, selected using stratified random sampling. In the simulation, we allow a fully observed Poisson distributed stratification criterion to be correlated with the partially observed model responses and develop five generalised structural equation growth models that host the auxiliary information from this criterion. We compare these models with each other and with a weighted growth model in terms of bias, efficiency, and coverage. We finally apply our best performing model to SCALES data and show how to obtain growth parameters and population norms. Results Parameter estimation from a model that incorporates a non-Normal auxiliary variable is unbiased and more efficient than its weighted counterpart. The auxiliary variable method is capable of producing efficient population percentile norms and velocities. Conclusions The deployment of a fully observed variable that dominates the selection of the sample and correlates strongly with the incomplete variable of interest appears beneficial for the estimation process.

Regression-type estimators for adaptive two-stage sequential sampling

2013 ◽  
Vol 20 (4) ◽  
pp. 571-590 ◽  
Author(s):  
Mohammad Salehi ◽  
Bardia Panahbehagh ◽  
Afshin Parvardeh ◽  
David R. Smith ◽  
Yuancai Lei
Keyword(s):  
Sequential Sampling ◽  
Two Stage
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