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Статті в журналах з теми "Two stage sampling design"

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Автор: Grafiati

Опубліковано: 8 вересня 2023

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1

Skinner, C. J. "Design Effects of Two-Stage Sampling." Journal of the Royal Statistical Society: Series B (Methodological) 48, no. 1 (September 1986): 89–99. http://dx.doi.org/10.1111/j.2517-6161.1986.tb01393.x.

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2

Rodrigues, Josemar. "Asymptotically design-unbiased predictors for two-stage sampling." Statistics & Probability Letters 17, no. 5 (August 1993): 411–14. http://dx.doi.org/10.1016/0167-7152(93)90263-i.

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3

Ugwu, Michael C., and Mbanefo S. Madukaife. "Two-stage cluster sampling with unequal probability sampling in the first stage and ranked set sampling in the second stage." Statistics in Transition New Series 23, no. 3 (September 1, 2022): 199–214. http://dx.doi.org/10.2478/stattrans-2022-0038.

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Abstract In this research work we introduce a new sampling design, namely a two-stage cluster sampling, where probability proportional to size with replacement is used in the first stage unit and ranked set sampling in the second in order to address the issue of marked variability in the sizes of population units concerned with first stage sampling. We obtained an unbiased estimator of the population mean and total, as well as the variance of the mean estimator. We calculated the relative efficiency of the new sampling design to the two-stage cluster sampling with simple random sampling in the first stage and ranked set sampling in the second stage. The results demonstrated that the new sampling design is more efficient than the competing design when a significant variation is observed in the first stage units.

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4

Chauvet, Guillaume, and Audrey-Anne Vallée. "Inference for two-stage sampling designs." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 82, no. 3 (May 6, 2020): 797–815. http://dx.doi.org/10.1111/rssb.12368.

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5

Raju, C., and R. Vidya. "DESIGN OF TWO-STAGE CHAIN SAMPLING PLANS WITH KULLBACK-LEIBLER INFORMATION." Advances and Applications in Statistics 73 (January 10, 2022): 47–74. http://dx.doi.org/10.17654/0972361722010.

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6

Valliant, Richard, Jill A. Dever, and Frauke Kreuter. "Effects of Cluster Sizes on Variance Components in Two-Stage Sampling." Journal of Official Statistics 31, no. 4 (December 1, 2015): 763–82. http://dx.doi.org/10.1515/jos-2015-0044.

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Abstract Determining sample sizes in multistage samples requires variance components for each stage of selection. The relative sizes of the variance components in a cluster sample are dramatically affected by how much the clusters vary in size, by the type of sample design, and by the form of estimator used. Measures of the homogeneity of survey variables within clusters are related to the variance components and affect the numbers of sample units that should be selected at each stage to achieve the desired precision levels. Measures of homogeneity can be estimated using standard software for random-effects models but the model-based intracluster correlations may need to be transformed to be appropriate for use with the sample design. We illustrate these points and implications for sample size calculation for two-stage sample designs using a realistic population derived from household surveys and the decennial census in the U.S.

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7

Taconeli, Cesar Augusto, and Angelo da Silva Cabral. "New two-stage sampling designs based on neoteric ranked set sampling." Journal of Statistical Computation and Simulation 89, no. 2 (November 15, 2018): 232–48. http://dx.doi.org/10.1080/00949655.2018.1541990.

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8

Molefe, Wilford. "Optimal allocation for equal probability two-stage design." Statistics in Transition New Series 23, no. 4 (December 1, 2022): 129–48. http://dx.doi.org/10.2478/stattrans-2022-0046.

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Abstract This paper develops optimal designs when it is not feasible for every cluster to be represented in a sample as in stratified design, by assuming equal probability two-stage sampling where clusters are small areas. The paper develops allocation methods for two-stage sample surveys where small-area estimates are a priority. We seek efficient allocations where the aim is to minimize the linear combination of the mean squared errors of composite small area estimators and of an estimator of the overall mean. We suggest some alternative allocations with a view to minimizing the same objective. Several alternatives, including the area-only stratified design, are found to perform nearly as well as the optimal allocation but with better practical properties. Designs are evaluated numerically using Switzerland canton data as well as Botswana administrative districts data.

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9

Hsu, Lie-Fern, and Jia-Tzer Hsu. "Economic Design of Acceptance Sampling Plans in a Two-Stage Supply Chain." Advances in Decision Sciences 2012 (February 29, 2012): 1–14. http://dx.doi.org/10.1155/2012/359082.

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Supply Chain Management, which is concerned with material and information flows between facilities and the final customers, has been considered the most popular operations strategy for improving organizational competitiveness nowadays. With the advanced development of computer technology, it is getting easier to derive an acceptance sampling plan satisfying both the producer's and consumer's quality and risk requirements. However, all the available QC tables and computer software determine the sampling plan on a noneconomic basis. In this paper, we design an economic model to determine the optimal sampling plan in a two-stage supply chain that minimizes the producer's and the consumer's total quality cost while satisfying both the producer's and consumer's quality and risk requirements. Numerical examples show that the optimal sampling plan is quite sensitive to the producer's product quality. The product's inspection, internal failure, and postsale failure costs also have an effect on the optimal sampling plan.

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10

Waters, James R., and Alexander J. Chester. "Optimal Allocation in Multivariate, Two-Stage Sampling Designs." American Statistician 41, no. 1 (February 1987): 46. http://dx.doi.org/10.2307/2684319.

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11

Waters, James R., and Alexander J. Chester. "Optimal Allocation in Multivariate, Two-Stage Sampling Designs." American Statistician 41, no. 1 (February 1987): 46–50. http://dx.doi.org/10.1080/00031305.1987.10475441.

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12

Thomas, Duncan, Rongrong Xie, and Mulugeta Gebregziabher. "Two-Stage sampling designs for gene association studies." Genetic Epidemiology 27, no. 4 (2004): 401–14. http://dx.doi.org/10.1002/gepi.20047.

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13

Brown, Jennifer A., Mohammad Salehi M., Mohammad Moradi, Gavin Bell, and David R. Smith. "An adaptive two-stage sequential design for sampling rare and clustered populations." Population Ecology 50, no. 3 (May 21, 2008): 239–45. http://dx.doi.org/10.1007/s10144-008-0089-1.

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14

Ozturk, Omer. "Two-stage cluster samples with ranked set sampling designs." Annals of the Institute of Statistical Mathematics 71, no. 1 (October 24, 2017): 63–91. http://dx.doi.org/10.1007/s10463-017-0623-z.

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15

Mwangi, Jesse Wachira, and Mohamed Esha Salim. "Efficiency of Two-Stage Adaptive Cluster Sampling Design in Estimating Fringe-Eared Oryx." Open Journal of Statistics 02, no. 05 (2012): 474–77. http://dx.doi.org/10.4236/ojs.2012.25060.

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16

Chakrabarty, Tapan Kr, B. M. Singh, and T. P. Tripathi. "Estimation of Finite Population Transition Probability Matrix Based on Two Stage Sampling Design." Calcutta Statistical Association Bulletin 49, no. 3-4 (September 1999): 215–24. http://dx.doi.org/10.1177/0008068319990308.

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17

Mandallaz, Daniel, and Ronghua Ye. "Forest inventory with optimal two-phase two-stage sampling schemes based on the anticipated variance." Canadian Journal of Forest Research 29, no. 11 (December 1, 1999): 1691–708. http://dx.doi.org/10.1139/x99-124.

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This work presents optimal sampling schemes for forest inventory. The sampling procedures are optimal in the sense that they minimize the anticipated variance for given costs or conversely, the anticipated variance is the average of the design-based variance under a local Poisson model for the spatial distribution of the trees. The resulting optimal inclusion rules are either probability proportional to size, in one-stage procedures, or a combination of probability proportional to prediction and probability proportional to error, in two-stage procedures. Best feasible approximations of the exact optimal sampling schemes are also given.

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18

Pramod Kumar Moury , Tauqueer Ahmad, Pramod Kumar Moury ,. Tauqueer Ahmad. "Estimation of Cotton Yield at Tehsil Level using Double Sampling Approach under Stratified Two Stage Sampling Design Framework." International Journal of Agricultural Science and Research 7, no. 5 (2017): 249–56. http://dx.doi.org/10.24247/ijasroct201730.

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19

Tate, J. E., and M. G. Hudgens. "Estimating Population Size with Two- and Three-Stage Sampling Designs." American Journal of Epidemiology 165, no. 11 (April 2, 2007): 1314–20. http://dx.doi.org/10.1093/aje/kwm005.

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20

Mukhopadhyay, Nitis, Mun S. Son, and Yong C. Ko. "A new two-stage sampling design for estimating the maximum average time to flower." Journal of Agricultural, Biological, and Environmental Statistics 9, no. 4 (December 2004): 479–99. http://dx.doi.org/10.1198/108571104x16196.

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21

Zhou, Haibo, Rui Song, Yuanshan Wu, and Jing Qin. "Statistical Inference for a Two-Stage Outcome-Dependent Sampling Design with a Continuous Outcome." Biometrics 67, no. 1 (June 16, 2010): 194–202. http://dx.doi.org/10.1111/j.1541-0420.2010.01446.x.

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22

Bandyopadhyay, Uttam, and Rahul Bhattacharya. "Testing Equality of Treatment Effects in a Two-stage Censored Allocation Design." Calcutta Statistical Association Bulletin 69, no. 2 (October 26, 2017): 132–49. http://dx.doi.org/10.1177/0008068317722279.

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Tests for the equality of treatment effects are developed for a two-stage sampling design with failure censoring in each stage. Depending on the results in the first stage, adaptive failure censoring is adopted in the second stage. Assuming exponential responses, tests for comparing the response distributions are obtained and some related small sample and asymptotic properties are worked out. Relevant competitor is also suggested and compared with the proposed procedure.

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23

Schweigert, J. F., C. W. Haegele, and M. Stocker. "Optimizing Sampling Design for Herring Spawn Surveys in the Strait of Georgia, B.C." Canadian Journal of Fisheries and Aquatic Sciences 42, no. 11 (November 1, 1985): 1806–14. http://dx.doi.org/10.1139/f85-226.

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Three estimators for two-stage sampling designs assuming unequal sized primaries (transects) were compared. The ratio estimator was found to provide the most consistent estimates of the mean and variance and so was used for estimating optimal sample design. Preliminary results from some biased sampling during 1976 and 1978 provided guidelines for the 1981 study designed to derive an optimal sampling design. Inconsistent results from the two areas surveyed during 1981 prevented general conclusions, but a corroborating resurvey of one area in 1983 suggested that a sampling intensity of five samples per 100 m of transect and transects every 250–400 m along the length of the spawn should result in estimates of the mean egg density with a standard error no greater than 25%. Systematic sampling is logistically preferable to random sampling and can be incorporated into the two-stage design described herein which should be used in future spawn surveys designed to estimate spawning escapement.

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24

Fan, Xiang Ning, Hao Zheng, Yu Tao Sun, and Xiang Yan. "Design and Implementation of a 12-Bit 100MS/s ADC." Applied Mechanics and Materials 229-231 (November 2012): 1507–10. http://dx.doi.org/10.4028/www.scientific.net/amm.229-231.1507.

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In this paper, a 12-bit 100MS/s pipelined ADC is designed. Capacitance flip-around structure is used in sample and hold circuit, and bootstrap structure is adopted in sampling switch which has high linearity. Progressively decreasing technology is used to reduce power consumption and circuit area, where 2.5bit/stage structure is used in the first two stages, 1.5bit/stage structure is used for 3rd to 8th stages, and at the end of the circuit is a 2bit-flash ADC. Digital calibration is designed to eliminate the offset of comparators. Switched-capacitor dynamic comparator structure is used to further reduce the power consumption. The ADC is implemented by using TSMC 0.18m CMOS process with die area be 1.23mm×2.3mm. SNDR and SFDR are 65dB and 71.3dB, when sampling at 100MHz sampling clock. The current of the circuit is 96mA under 1.8V power supply.

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25

Chen, Sixia, David Haziza, and Zeinab Mashreghi. "A Comparison of Existing Bootstrap Algorithms for Multi-Stage Sampling Designs." Stats 5, no. 2 (June 6, 2022): 521–37. http://dx.doi.org/10.3390/stats5020031.

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Multi-stage sampling designs are often used in household surveys because a sampling frame of elements may not be available or for cost considerations when data collection involves face-to-face interviews. In this context, variance estimation is a complex task as it relies on the availability of second-order inclusion probabilities at each stage. To cope with this issue, several bootstrap algorithms have been proposed in the literature in the context of a two-stage sampling design. In this paper, we describe some of these algorithms and compare them empirically in terms of bias, stability, and coverage probability.

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26

Salehi, Mohammad, and David R. Smith. "Adaptive two-stage inverse sampling design to estimate density, abundance, and occupancy of rare and clustered populations." PLOS ONE 16, no. 8 (August 18, 2021): e0255256. http://dx.doi.org/10.1371/journal.pone.0255256.

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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. We take advantage of the characteristics of inverse sampling to adaptively inform practitioners when it is efficient to move on to sample new areas. We introduce Adaptive Two-stage Inverse Sampling (ATIS), which is designed to leave a selected area after observation of an a priori number of only non-rare units and to continue sampling in the area when rare units are observed. ATIS is efficient in many cases and yields more rare units than conventional sampling for a rare and clustered population. We derive unbiased estimators of population total and variance. We also introduce an easy-to-compute estimator, which is nearly as efficient as the unbiased estimator. A simulation study on a rare plant population of buttercups (Ranunculus) shows that ATIS even with the easy-to-compute estimator is more efficient than its conventional sampling counterparts and is more efficient than Two-stage Adaptive Cluster Sampling (TACS) for small and moderate final sample sizes. Additional simulations reveal that ATIS is efficient for binary data (e.g., presence or absence) whereas TACS is inefficient for binary data. The overall results indicate that ATIS is consistently efficient compared to conventional sampling and to adaptive cluster sampling in some important cases.

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27

Crone, Paul R. "Sampling design and statistical considerations for the commercial groundfish fishery of Oregon." Canadian Journal of Fisheries and Aquatic Sciences 52, no. 4 (April 1, 1995): 716–32. http://dx.doi.org/10.1139/f95-072.

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Fisheries management is often based on data collected through various sample survey programs. At a minimum, commercial fisheries must be monitored to determine the species and age compositions of the landings; this provides the baseline data on which to assess the stocks. An equally important objective, which is often ignored, is the determination of the variability associated with derived estimates. This paper presents measures of dispersion for landing estimates generated from a two-stage sampling design used for commercially harvested groundfish species landed at Oregon ports; particular attention is given to the relative magnitudes of variability at the first and second stages of sampling designs used for estimating species and age compositions. At least two-thirds of the total amount of rockfish landed in each port/quarter stratum consisted of estimates of species composition that had small coefficients of variation (CV < 10%). For each species sampled for age composition, at least three-fourths of the total landings included estimates for individual ages with CVs < 25%. For the majority of the landings, the variation at the first stage of sampling contributed at least 63 and 90% to the variance of the estimates for the species and age compositions, respectively.

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28

Fallahnezhad, MS, and E. Qazvini. "A new economical scheme of acceptance sampling plan in a two-stage approach based on the maxima nomination sampling technique." Transactions of the Institute of Measurement and Control 39, no. 7 (February 15, 2016): 1097–103. http://dx.doi.org/10.1177/0142331216629203.

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An acceptance sampling plan plays a very important role in any quality assurance system. In this new economical design of acceptance sampling plan, three types of costs are included in the objective function by considering average outgoing quality limit (AOQL), average quality level (AQL) and lot tolerance percent defective (LTPD) constraints based on the maxima nomination sampling (MNS) method in a two-stage approach. The design of this sampling inspection plan involves the minimum average total inspection (ATI). The model is designed to minimize the summation of costs and the proposed MNS economical sampling plan is compared with the classical one. Practitioners can use the proposed model to decrease the total cost of inspection.

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29

Whittemore, Alice S., and Jerry Halpern. "Two-stage sampling designs for external validation of personal risk models." Statistical Methods in Medical Research 25, no. 4 (July 11, 2016): 1313–29. http://dx.doi.org/10.1177/0962280213480420.

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30

Wagner, Bruce, and Mo D. Salman. "Strategies for two-stage sampling designs for estimating herd-level prevalence." Preventive Veterinary Medicine 66, no. 1-4 (December 2004): 1–17. http://dx.doi.org/10.1016/j.prevetmed.2004.07.008.

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31

Farver, Thomas B. "Disease prevalence estimation in animal populations using two-stage sampling designs." Preventive Veterinary Medicine 5, no. 1 (August 1987): 1–20. http://dx.doi.org/10.1016/0167-5877(87)90002-x.

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32

Molloy, Kevin, Laurent Denarie, Marc Vaisset, Thierry Siméon, and Juan Cortés. "Simultaneous system design and path planning: A sampling-based algorithm." International Journal of Robotics Research 38, no. 2-3 (July 9, 2018): 375–87. http://dx.doi.org/10.1177/0278364918783054.

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This paper addresses the simultaneous design and path-planning problem, in which features associated to the bodies of a mobile system must be selected to find the best design that optimizes its motion between two given configurations. Solving individual path-planning problems for all possible designs and selecting the best result would be straightforward only for very simple cases. We propose a more efficient approach that combines discrete (design) and continuous (path) optimization in a single stage. It builds on an extension of a sampling-based algorithm, which simultaneously explores the configuration-space costmap of all possible designs, aiming to find the best path-design pair. The algorithm filters out unsuitable designs during the path search, which breaks down the combinatorial explosion. Illustrative results are presented for relatively simple (academic) robotic examples, showing that even in these simple cases, the computational cost can be reduced by two orders of magnitude with respect to the naïve approach. A preliminary application to challenging problems in computational biology related to protein design is also discussed.

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33

Niklitschek, Mario E., and Guillermo Trincado. "A cost effective stratified two-stage sampling design to estimate the forest land area of southern Chile." Canadian Journal of Forest Research 41, no. 7 (July 2011): 1509–21. http://dx.doi.org/10.1139/x11-040.

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There is a growing demand for improving the measurement of forest resources, with more frequent updating and better information on environmental variables. We explore the cost efficiency of a stratified two-stage design using area sampling to estimate the forest plantation and native forest areas in southern Chile. Analytical expressions for the approximate mean square error of combined and separate ratio estimators are derived applying Taylor linearization. Under a unified framework, this procedure allows the evaluation of the precision of design and post-design estimators for unequal unit area sizes at both stages. Monte Carlo simulations were used to assess empirically the approximate analytical measures of the mean square error and the biases associated with the ratio estimators. Adopting proportional allocation among strata and clusters, the optimal allocation among the two stages is determined. A substantial improvement in sampling precision was achieved using the separate ratio estimator and the bias was found to be small. Post-stratification based on categorical information on growing zones also improved the precision of estimating the forest plantation area and a smaller extent the native forest area. The results of this paper support a wider adoption of sampling methods to estimate land use and land cover at regional or national levels.

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34

Chaudhuri, Arijit. "Estimation from an Under-Covered Sample in a Complex Survey for Auditing." Calcutta Statistical Association Bulletin 54, no. 1-2 (March 2003): 115–20. http://dx.doi.org/10.1177/0008068320030110.

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We encounter a practical problem when from a finite population a complex survey design justifies selection of a sample in five stages. The design stipulates selecting a specified number of n(> 2) first stage units by Rao, Hartley and Cochran's (RHC, 1962) scheme followed by simple random sampling (SRS) without replacement (WOR) in each of the four subsequent stages. At the implementation stage a resource crunch calls for reducing the first stage sample size from n to m ( m < n). The consequential modifications in the methods of estimation of population total, mean and ratio of two totals and those of the variance of the first and the mean square errors of the latter two estimators are spelt out. The problem arises in the context of designing audits of Government documents by appropriate sampling.

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35

Yan, Cheng, Xiuli Shen, and Fushui Guo. "Novel Two-Stage Method for Low-Order Polynomial Model." Mathematical Problems in Engineering 2018 (July 4, 2018): 1–13. http://dx.doi.org/10.1155/2018/8156390.

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One of the most popular statistical models is a low-order polynomial response surface model, i.e., a polynomial of first order or second order. These polynomials can be used for global metamodels in weakly nonlinear simulation to approximate their global tendency and local metamodels in response surface methodology (RSM), which has been studied in various applications in engineering design and analysis. The order of the selected polynomial determines the number of sampling points (input combinations) and the resulting accuracy (validity, adequacy). This paper derives a novel method to obtain an accurate high-order polynomial while requiring fewer sampling points. This method uses a two-stage procedure such that the second stage modifies the low-order polynomial estimated in the first stage; this second stage does not require new points. This paper evaluates the performance of the method numerically by using several test functions. These numerical results show that the proposed method can provide more accurate predictions than the traditional method.

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36

Baquero, Oswaldo Santos, Marcos Amaku, Ricardo Augusto Dias, José Henrique Hildebrand Grisi Filho, José Soares Ferreira Neto, and Fernando Ferreira. "Validity of a two-stage cluster sampling design to estimate the total number of owned dogs." Preventive Veterinary Medicine 151 (March 2018): 40–45. http://dx.doi.org/10.1016/j.prevetmed.2017.12.017.

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37

Saini, Monika, and Shashi Bahl. "Estimation of Population Mean in Two Stage Design using Double Sampling for Stratification and Multiauxiliary Information." International Journal of Computer Applications 47, no. 9 (June 30, 2012): 17–21. http://dx.doi.org/10.5120/7216-0011.

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38

Zhou, Qingning, Jianwen Cai, and Haibo Zhou. "Semiparametric inference for a two-stage outcome-dependent sampling design with interval-censored failure time data." Lifetime Data Analysis 26, no. 1 (January 7, 2019): 85–108. http://dx.doi.org/10.1007/s10985-019-09461-5.

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39

Zheng, Ming, and Wen Yu. "An empirical likelihood approach to data analysis under two-stage sampling designs." Statistics & Probability Letters 81, no. 8 (August 2011): 947–56. http://dx.doi.org/10.1016/j.spl.2011.01.011.

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40

Puerta, Patricia, Lorenzo Ciannelli, and Bethany Johnson. "A simulation framework for evaluating multi-stage sampling designs in populations with spatially structured traits." PeerJ 7 (February 25, 2019): e6471. http://dx.doi.org/10.7717/peerj.6471.

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Selecting an appropriate and efficient sampling strategy in biological surveys is a major concern in ecological research, particularly when the population abundance and individual traits of the sampled population are highly structured over space. Multi-stage sampling designs typically present sampling sites as primary units. However, to collect trait data, such as age or maturity, only a sub-sample of individuals collected in the sampling site is retained. Therefore, not only the sampling design, but also the sub-sampling strategy can have a major impact on important population estimates, commonly used as reference points for management and conservation. We developed a simulation framework to evaluate sub-sampling strategies from multi-stage biological surveys. Specifically, we compare quantitatively precision and bias of the population estimates obtained using two common but contrasting sub-sampling strategies: the random and the stratified designs. The sub-sampling strategy evaluation was applied to age data collection of a virtual fish population that has the same statistical and biological characteristics of the Eastern Bering Sea population of Pacific cod. The simulation scheme allowed us to incorporate contributions of several sources of error and to analyze the sensitivity of the different strategies in the population estimates. We found that, on average across all scenarios tested, the main differences between sub-sampling designs arise from the inability of the stratified design to reproduce spatial patterns of the individual traits. However, differences between the sub-sampling strategies in other population estimates may be small, particularly when large sub-sample sizes are used. On isolated scenarios (representative of specific environmental or demographic conditions), the random sub-sampling provided better precision in all population estimates analyzed. The sensitivity analysis revealed the important contribution of spatial autocorrelation in the error of population trait estimates, regardless of the sub-sampling design. This framework will be a useful tool for monitoring and assessment of natural populations with spatially structured traits in multi-stage sampling designs.

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Jiang, Chen, Xiwen Cai, Haobo Qiu, Liang Gao, and Peigen Li. "A two-stage support vector regression assisted sequential sampling approach for global metamodeling." Structural and Multidisciplinary Optimization 58, no. 4 (April 23, 2018): 1657–72. http://dx.doi.org/10.1007/s00158-018-1992-5.

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42

Chester, Alexander J., and James R. Waters. "Two-Stage Sampling for Age Distribution in the Atlantic Menhaden Fishery, with Comments on Optimal Survey Design." North American Journal of Fisheries Management 5, no. 3B (July 1985): 449–56. http://dx.doi.org/10.1577/1548-8659(1985)5<449:tsfadi>2.0.co;2.

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43

Mandallaz, Daniel. "Design-based properties of some small-area estimators in forest inventory with two-phase sampling." Canadian Journal of Forest Research 43, no. 5 (May 2013): 441–49. http://dx.doi.org/10.1139/cjfr-2012-0381.

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We consider the small-area estimation problem for forest inventories with two-phase sampling schemes. We propose an improvement to the synthetic estimator, when the true mean of the auxiliary variables over the small area is unknown and must be estimated, and likewise to the residual corrected small-area estimator. We derive the asymptotic design-based variances of these new estimators, the pseudo-synthetic and pseudo-small-area estimators, by also incorporating the design-based variance of the regression coefficients. We then propose a very simple mathematical device that transforms pseudo-small-area estimators into pseudo-synthetic estimators, which is very convenient for deriving asymptotic variances. The results are extended to cluster and two-stage sampling at the plot level. A case study and a simulation illustrate the theory.

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Temesgen, H. "Evaluation of sampling alternatives to quantify tree leaf area." Canadian Journal of Forest Research 33, no. 1 (January 1, 2003): 82–95. http://dx.doi.org/10.1139/x02-159.

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High within- and among-tree crown variation have contributed to the difficulty of tree-crown sampling and single-tree leaf area (area available for photosynthesis) estimation. Using reconstructed trees, simulations were used to compare five sampling designs for bias, mean square error (MSE), and distribution of the estimates. All sampling designs showed nearly zero bias. For most sample trees, stratified random sampling resulted in the lowest MSE values, followed by ellipsoidal, two-stage systematic, simple random, and then by two-stage unequal probability sampling. The poor performance of two-stage unequal probability sampling can be ascribed to the unequal probability of inclusion of first-order branches and twigs.

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Horppila, Jukka, and Heikki Peltonen. "Optimizing Sampling from Trawl Catches: Contemporaneous Multistage Sampling for Age and Length structures." Canadian Journal of Fisheries and Aquatic Sciences 49, no. 8 (August 1, 1992): 1555–59. http://dx.doi.org/10.1139/f92-172.

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The objective of this study was to find out the optimal sampling strategy for contemporaneous multistage sampling of age and length structures of trawl catches. Samples were taken from a roach (Rutilus rutilus) stock of Lake Vesijärvi, southern Finland. Two-stage sampling proved to be superior to three-stage sampling (i.e. subsamples from the trawl catches are unnecessary). Assuming that all the age and length groups are equally important, the optimal strategy is to sample 34 fish from each of 46 loads. Compared with the present scheme (500 fish from each of 10 loads), this design cuts down the total variance of the estimated proportions of different age and length groups to about one third. An age–length key was used when estimating the proportions of the age groups. The benefits of the applied age–length key were obvious. The estimates of the proportions of different age groups were more accurate than those obtained with age readings only. Increasing the fraction of age-determined fish increases the costs, diminishes the optimal number of samples, and consequently increases the variance of the estimated proportions of different groups.

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Brahim, MAHIDDINI. "Optimum design of a spur gear using a two level optimization approach." Mechanics 25, no. 4 (August 28, 2019): 304–12. http://dx.doi.org/10.5755/j01.mech.25.4.18994.

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In this paper, we present a two level optimization approach in order to enhance the design process of a one-stage speed reducer. The proposed design methodology is performed using genetic algorithms which are judiciously combined with the use of :i) analytical models (1stlevel) and ii) Finite Element Method (FEM)based models ( 2nd level), to evaluate design candidates. Indeed, the use of CAD-CAE tools to develop higher fidelity FEM models allows to re-evaluate the attained first level designs, while accounting for new design parameters and advanced aspects which have been ignored in the first level. In order to minimize the computational burden, a metamodel based optimization technique is adopted at this second level. To illustrate the efficiency of the proposed approach, a case study of a spur gear based reducer is presented where the design of experiments is built using Hypercube Latin Sampling and surrogate models are constructed using Radial Basic Functions.

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Nicoletti, João V. M., Marcello R. A. Franchi, Anamari V. de A. Motomiya, Wagner R. Motomiya, and José P. Molin. "Efficiency and quality of soil sampling according to a sampling tool." Revista Brasileira de Engenharia Agrícola e Ambiental 27, no. 6 (June 2023): 480–86. http://dx.doi.org/10.1590/1807-1929/agriambi.v27n6p480-486.

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ABSTRACT Soil sampling is a fundamental stage for recommending agricultural correctives and fertilizers, estimating the nutritional demands of plants, and consequently maximizing productivity. Therefore, this study aimed to assess the performance of three soil samplers in different management systems in terms of sample quality and operational efficiency. A completely randomized experimental design was used in a factorial scheme. Three samplers and two sampling depths (3 × 2) were used with four replicates. At each sampling location, eight single samples were taken at a varying sampling depth of 0.0-0.2 and 0.2-0.4 m, and the collection time was recorded. Samples were analyzed for chemical attributes and granulometry. Statistically significant differences were observed for specific attributes (organic matter, K, Ca, CEC, pH, and S). In terms of operational efficiency, the hydraulic sampler was more efficient than the other samplers, being three times faster than the combustion drill and six times faster than the manual probe. Thus, it is suitable and reliable for soil sampling purposes.

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Xu, W., and H. Zhou. "Mixed effect regression analysis for a cluster-based two-stage outcome-auxiliary-dependent sampling design with a continuous outcome." Biostatistics 13, no. 4 (June 21, 2012): 650–64. http://dx.doi.org/10.1093/biostatistics/kxs013.

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Yang, D. Aaron, and Richard A. Laven. "Design-Based Approach for Analysing Survey Data in Veterinary Research." Veterinary Sciences 8, no. 6 (June 8, 2021): 105. http://dx.doi.org/10.3390/vetsci8060105.

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Sample surveys are an essential approach used in veterinary research and investigation. A sample obtained from a well-designed sampling process along with robust data analysis can provide valuable insight into the attributes of the target population. Two approaches, design-based or model-based, can be used as inferential frameworks for analysing survey data. Compared to the model-based approach, the design-based approach is usually more straightforward and directly makes inferences about the finite target population (such as the dairy cows in a herd or dogs in a region) rather than an infinite superpopulation. In this paper, the concept of probability sampling and the design-based approach is briefly reviewed, followed by a discussion of the estimations and their justifications in the context of several different elementary sampling methods, including simple random sampling, stratified random sampling, and one-stage cluster sampling. Finally, a concrete example of a complex survey design (involving multistage sampling and stratification) is demonstrated, illustrating how finding unbiased estimators and their corresponding variance formulas for a complex survey builds on the techniques used in elementary sampling methods.

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WANG, GUANGHUI, PENG LI, and JING ZHOU. "A DUAL-STAGE SAMPLING BASED ARTIFICIAL NEURAL NETWORK RESPONSE SURFACE METHODOLOGY AND ITS APPLICATION TO TUBULAR PERMANENT-MAGNET LINEAR SYNCHRONOUS MOTOR." International Journal of Computational Intelligence and Applications 13, no. 03 (September 2014): 1450015. http://dx.doi.org/10.1142/s1469026814500151.

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A dual-stage sampling (DSS) is proposed to reduce training samples of artificial neural network for establishing the analytical response surface model. The procedure of the DSS-based artificial neural network response surface (ANNRS) methodology is divided into two stages. At the rough stage, new sample is generated in the entire space by maximizing the minimum distance to existed samples. Then at the refine stage, new sample is produced in the same way, but is limited in the partial space between two samples with larger gradient and larger distance. And the efficiency of the DSS method is proved by a test function. Considering design variables about slot width and depth, permanent magnet (PM) width and height, and end tooth width, tubular permanent-magnet linear synchronous motor (TPMLSM) modeling problem is represented, whose thrust and ripple are calculated time-consumingly by finite element analysis. Then, The DSS-based ANNRS is applied to establish the TPMLSM model in good accuracy with fewer samples, lower consumption.

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