Results 11 to 20 of about 646,279 (275)
Variance estimation in stratified adaptive cluster sampling
Statistics in Transition New Series, 2022 In many sampling surveys, the use of auxiliary information at either the design or estimation stage, or at both these stages is usual practice. Auxiliary information is commonly used to obtain improved designs and to achieve a high level of precision in ...
Uzma Yasmeen +2 more
semanticscholar +1 more source
Adaptive Cluster Sampling-Based Design for Estimating COVID-19 Cases With Random Samples
Current Science, 2021 During the COVID-19 pandemic, testing of all persons except those who are symptomatic, is not feasible due to shortage of facilities and staff This article focuses on estimating the number of COVID-19-positive persons over a geographical domain The ...
Girish Chandra +2 more
semanticscholar +1 more source
Model-based Inference for Rare and Clustered Populations from Adaptive Cluster Sampling using Auxiliary Variables [PDF]
Journal of Survey Statistics and Methodology, 2020 Rare populations, such as endangered animals and plants, drug users and individuals with rare diseases, tend to cluster in regions. Adaptive cluster sampling is generally applied to obtain information from clustered and sparse populations since it ...
Izabel Nolau de Souza +2 more
semanticscholar +1 more source
Arithmetic, 2023 This research aims to determine the effect of the CPS learning model on mathematical adaptive reasoning ability in terms of students' entrepreneurial character.
Komarudin Komarudin +4 more
doaj +1 more source
PLoS ONE, 2021 Sampling rare and clustered populations is challenging because of the effort required to find rare units. Heuristically, a practitioner would prefer to discontinue sampling in areas where rare units of interest are apparently extremely sparse or absent ...
Mohammad Salehi, David R Smith
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Fast and interpretable consensus clustering via minipatch learning.
PLoS Computational Biology, 2022 Consensus clustering has been widely used in bioinformatics and other applications to improve the accuracy, stability and reliability of clustering results.
Luqin Gan, Genevera I Allen
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DisSAGD: A Distributed Parameter Update Scheme Based on Variance Reduction
Sensors, 2021 Machine learning models often converge slowly and are unstable due to the significant variance of random data when using a sample estimate gradient in SGD.
Haijie Pan, Lirong Zheng
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On two-stage adaptive cluster sampling to assess pest density
浙江大学学报. 农业与生命科学版, 2000 The adaptive cluster sampling introduced by Thompson is a powerful method for a survey of a population which is highly clumped with clumps widely separated.
ZHANG Nan-song +2 more
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Inventory of sparse forest populations using adaptive cluster sampling
Silva Fennica, 2006 In many studies, adaptive cluster sampling (ACS) proved to be a powerful tool for assessing rare clustered populations that are difficult to estimate by means of conventional sampling methods.
Talvitie, Mervi +2 more
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Ratio Estimator in Adaptive Cluster Sampling without Replacement of Networks
Journal of Probability and Statistics, 2014 In this paper, we study the estimators of the population total in adaptive cluster sampling by using the information of the auxiliary variable. The numerical examples showed that the ratio estimator in adaptive cluster sampling without replacement of ...
Nipaporn Chutiman, Monchaya Chiangpradit
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