Relevant bibliographies by topics / Second phase sampling

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  1. Journal articles

Academic literature on the topic 'Second phase sampling'

Author: Grafiati
Published: 8 September 2023

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Journal articles on the topic "Second phase sampling"

1

Delmelle, Eric M., and Pierre Goovaerts. "Second-phase sampling designs for non-stationary spatial variables." Geoderma 153, no. 1-2 (2009): 205–16. http://dx.doi.org/10.1016/j.geoderma.2009.08.007.

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2

Li, H. G., H. T. Schreuder, D. D. Van Hooser, and G. E. Brink. "Estimating Strata Means in Double Sampling with Corrections Based on Second-Phase Sampling." Biometrics 48, no. 1 (1992): 189. http://dx.doi.org/10.2307/2532749.

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3

Kamba, Adamu Isah, Amos Adedayo Adewara, and Audu Ahmed. "Modified product estimator under two–phase sampling." Global Journal of Pure and Applied Sciences 25, no. 2 (2019): 201–8. http://dx.doi.org/10.4314/gjpas.v25i2.10.

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In this paper, modification of product estimator under two-phase sampling was suggested. The modified product estimator was obtained through transformation in two cases using sample mean of auxiliary variables. Case one was when the second sample was drawn from the first sample while case two was when the second sample was drawn from the population. The bias and mean square error (MSE) of the modified product estimator was obtained. The theoretical and numerical validity of the modified product estimator under the two cases were determined to show it superiority to some considered existing product estimators. Numerical results shows that the modified product estimator under the two cases were more efficient than the considered existing estimators.Keywords: Product estimator, Two-Phase Sampling, Bias, Mean Square Error
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4

Singh, Rajesh, and Prayas Sharma. "Efficient Estimators Using Auxiliary Variable under Second Order Approximation in Simple Random Sampling and Two-Phase Sampling." Advances in Statistics 2014 (September 3, 2014): 1–9. http://dx.doi.org/10.1155/2014/974604.

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This paper suggests some estimators for population mean of the study variable in simple random sampling and two-phase sampling using information on an auxiliary variable under second order approximation. Bahl and Tuteja (1991) and Singh et al. (2008) proposed some efficient estimators and studied the properties of the estimators to the first order of approximation. In this paper, we have tried to find out the second order biases and mean square errors of these estimators using information on auxiliary variable based on simple random sampling and two-phase sampling. Finally, an empirical study is carried out to judge the merits of the estimators over others under first and second order of approximation.
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5

Ji, Chao, James D. Englehardt, and Cynthia Juyne Beegle-Krause. "Design of Real—Time Sampling Strategies for Submerged Oil Based on Probabilistic Model Predictions." Journal of Marine Science and Engineering 8, no. 12 (2020): 984. http://dx.doi.org/10.3390/jmse8120984.

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Locating and tracking submerged oil in the mid depths of the ocean is challenging during an oil spill response, due to the deep, wide-spread and long-lasting distributions of submerged oil. Due to the limited area that a ship or AUV can visit, efficient sampling methods are needed to reveal the real distributions of submerged oil. In this paper, several sampling plans are developed for collecting submerged oil samples using different sampling methods combined with forecasts by a submerged oil model, SOSim (Subsurface Oil Simulator). SOSim is a Bayesian probabilistic model that uses real time field oil concentration data as input to locate and forecast the movement of submerged oil. Sampling plans comprise two phases: the first phase for initial field data collection prior to SOSim assessments, and the second phase based on the SOSim assessments. Several environmental sampling techniques including the systematic random, modified station plans as well zig-zag patterns are evaluated for the first phase. The data using the first phase sampling plan are then input to SOSim to produce submerged oil distributions in time. The second phase sampling methods (systematic random combined with the kriging-based sampling method and naive zig-zag sampling method) are applied to design the sampling plans within the submerged oil area predicted by SOSim. The sampled data obtained using the second phase sampling methods are input to SOSim to update the model’s assessments. The performance of the sampling methods is evaluated by comparing SOSim predictions using the sampled data from the proposed sampling methods with simulated submerged oil distributions during the Deepwater Horizon spill by the OSCAR (oil spill contingency and response) oil spill model. The proposed sampling methods, coupled with the use of the SOSim model, are shown to provide an efficient approach to guide oil spill response efforts.
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6

Sang, Hailin, Kenneth K. Lopiano, Denise A. Abreu, Andrea C. Lamas, Pam Arroway, and Linda J. Young. "Adjusting for Misclassification: A Three-Phase Sampling Approach." Journal of Official Statistics 33, no. 1 (2017): 207–22. http://dx.doi.org/10.1515/jos-2017-0011.

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Abstract The United States Department of Agriculture’s National Agricultural Statistics Service (NASS) conducts the June Agricultural Survey (JAS) annually. Substantial misclassification occurs during the prescreening process and from field-estimating farm status for nonresponse and inaccessible records, resulting in a biased estimate of the number of US farms from the JAS. Here, the Annual Land Utilization Survey (ALUS) is proposed as a follow-on survey to the JAS to adjust the estimates of the number of US farms and other important variables. A three-phase survey design-based estimator is developed for the JAS-ALUS with nonresponse adjustment for the second phase (ALUS). A design-unbiased estimator of the variance is provided in explicit form.
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7

Mandallaz, Daniel. "A three-phase sampling extension of the generalized regression estimator with partially exhaustive information." Canadian Journal of Forest Research 44, no. 4 (2014): 383–88. http://dx.doi.org/10.1139/cjfr-2013-0449.

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We consider three-phase sampling schemes in which one component of the auxiliary information is known in the very large sample of the so-called null phase and the second component is available only in the large sample of the first phase, whereas the second phase provides the terrestrial inventory data. We extend to three-phase sampling the generalized regression estimator that applies when the null phase is exhaustive, for global and local estimation, and derive its asymptotic design-based variance. The new three-phase regression estimator is particularly useful for reducing substantially the computing time required to treat exhaustively very large data sets generated by modern remote sensing technology such as LiDAR.
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8

Fischer, Christoph, and Joachim Saborowski. "Variance estimation for mean growth from successive double sampling for stratification." Canadian Journal of Forest Research 50, no. 12 (2020): 1405–11. http://dx.doi.org/10.1139/cjfr-2020-0058.

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Double sampling for stratification (2SS) is a sampling design that is widely used for forest inventories. We present the mathematical derivation of two appropriate variance estimators for mean growth from repeated 2SS with updated stratification on each measurement occasion. Both estimators account for substratification based on the transition of sampling units among the strata due to the updated allocation. For the first estimator, sizes of the substrata were estimated from the second-phase sample (sample plots), whereas the respective sizes in the second variance estimator relied on the larger first-phase sample. The estimators were empirically compared with a modified version of Cochran’s well-known 2SS variance estimator that ignores substratification. This was done by performing bootstrap resampling on data from two German forest districts. The major findings were as follows: (i) accounting for substratification, as implemented in both new estimators, has substantial impact in terms of significantly smaller variance estimates and bias compared with the estimator without substratification, and (ii) the second estimator with substrata sizes being estimated from the first-phase sample shows a smaller bias than the first estimator.
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9

Šmelková, Ľ. "Inventory of plant material in forest nurseries by combining an ocular estimate and sampling measurements." Journal of Forest Science 48, No. 4 (2019): 156–65. http://dx.doi.org/10.17221/11869-jfs.

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Two procedures of the plant material inventories in forest nurseries, used until now, are evaluated: ocular estimate and sampling. A new two-phase sampling procedure has been proposed on the basis of a suitable combination of estimation and counting and/or measurement of seedlings and plants. The optimum size of the sampling unit (length of bed segment) has been defined. The necessary number of bed segments on which the ocular estimation should be performed in the first phase (n1), and subsequently a more exact assessment of the number of individuals and/or their other qualitative and quantitative traits should be done in the second phase (n2), to achieve the required precision of results of ± 2 to 10% with reliability of 95%. A theoretical justification of the proposal as well as a detailed procedure of the accomplishment is presented. The frames have been specified where the proposed method is economically twice as beneficial as the classic sampling method.
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10

Mandallaz, Daniel, Jochen Breschan, and Andreas Hill. "New regression estimators in forest inventories with two-phase sampling and partially exhaustive information: a design-based Monte Carlo approach with applications to small-area estimation." Canadian Journal of Forest Research 43, no. 11 (2013): 1023–31. http://dx.doi.org/10.1139/cjfr-2013-0181.

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We consider two-phase sampling schemes where one component of the auxiliary information is known in every point (“wall-to-wall”) and a second component is available only in the large sample of the first phase, whereas the second phase yields a subsample with the terrestrial inventory. This setup is of growing interest in forest inventory thanks to the recent advances in remote sensing, in particular, the availability of LiDAR data. We propose a new two-phase regression estimator for global and local estimation and derive its asymptotic design-based variance. The new estimator performs better than the classical regression estimator. Furthermore, it can be generalized to cluster sampling and two-stage tree sampling within plots. Simulations and a case study with LiDAR data illustrate the theory.
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