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Relevant bibliographies by topics / Three Phase sampling

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Journal articles

Academic literature on the topic 'Three Phase sampling'

Author: Grafiati

Published: 8 September 2023

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Journal articles on the topic "Three Phase sampling"

1

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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2

Magnussen, Steen. "Stepwise estimators for three-phase sampling of categorical variables." Journal of Applied Statistics 30, no. 5 (2003): 461–75. http://dx.doi.org/10.1080/0266476032000053628.

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3

Saliby, Eduardo, and Ray J. Paul. "Implementing Descriptive Sampling in Three-Phase Discrete Event Simulation Models." Journal of the Operational Research Society 44, no. 2 (1993): 147. http://dx.doi.org/10.2307/2584363.

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4

Carugati, Ignacio, Sebastian Maestri, Patricio G. Donato, Daniel Carrica, and Mario Benedetti. "Variable Sampling Period Filter PLL for Distorted Three-Phase Systems." IEEE Transactions on Power Electronics 27, no. 1 (2012): 321–30. http://dx.doi.org/10.1109/tpel.2011.2149542.

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Saliby, Eduardo, and Ray J. Paul. "Implementing Descriptive Sampling in Three-Phase Discrete Event Simulation Models." Journal of the Operational Research Society 44, no. 2 (1993): 147–60. http://dx.doi.org/10.1057/jors.1993.27.

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6

Lepage, Kyle Q., Mark A. Kramer, and Uri T. Eden. "Some Sampling Properties of Common Phase Estimators." Neural Computation 25, no. 4 (2013): 901–21. http://dx.doi.org/10.1162/neco_a_00422.

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The instantaneous phase of neural rhythms is important to many neuroscience-related studies. In this letter, we show that the statistical sampling properties of three instantaneous phase estimators commonly employed to analyze neuroscience data share common features, allowing an analytical investigation into their behavior. These three phase estimators—the Hilbert, complex Morlet, and discrete Fourier transform—are each shown to maximize the likelihood of the data, assuming the observation of different neural signals. This connection, explored with the use of a geometric argument, is used to describe the bias and variance properties of each of the phase estimators, their temporal dependence, and the effect of model misspecification. This analysis suggests how prior knowledge about a rhythmic signal can be used to improve the accuracy of phase estimates.

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7

Fattorini, Lorenzo, Marzia Marcheselli, and Caterina Pisani. "A three-phase sampling strategy for large-scale multiresource forest inventories." Journal of Agricultural, Biological, and Environmental Statistics 11, no. 3 (2006): 296–316. http://dx.doi.org/10.1198/108571106x130548.

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8

Becker, Benjamin, Christian Kochleus, Denise Spira, et al. "Passive sampler phases for pesticides: evaluation of AttractSPE™ SDB-RPS and HLB versus Empore™ SDB-RPS." Environmental Science and Pollution Research 28, no. 9 (2021): 11697–707. http://dx.doi.org/10.1007/s11356-020-12109-9.

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AbstractIn this study, three different passive sampling receiving phases were evaluated, with a main focus on the comparability of established styrene-divinylbenzene reversed phase sulfonated (SDB-RPS) sampling phase from Empore™ (E-RPS) and novel AttractSPE™ (A-RPS). Furthermore, AttractSPE™ hydrophilic-lipophilic balance (HLB) disks were tested. To support sampling phase selection for ongoing monitoring needs, it is important to have information on the characteristics of alternative phases. Three sets of passive samplers (days 1–7, days 8–14, and days 1–14) were exposed to a continuously exchanged mixture of creek and rainwater in a stream channel system under controlled conditions. The system was spiked with nine pesticides in two peak scenarios, with log KOW values ranging from approx. − 1 to 5. Three analytes were continuously spiked at a low concentration. All three sampling phases turned out to be suitable for the chosen analytes, and, in general, uptake rates were similar for all three materials, particularly for SDB-RPS phases. Exceptions concerned bentazon, where E-RPS sampled less than 20% compared with the other phases, and nicosulfuron, where HLB sampled noticeably more than both SDB-RPS phases. All three phases will work for environmental monitoring. They are very similar, but differences indicate one cannot just use literature calibration data and transfer these from one SDB phase to another, though for most compounds, it may work fine. Graphical abstract

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9

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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10

Mekhilef, Saad, Ahmad Maliki Omar, and Nasrudin Abd Rahim. "Modeling of Three-Phase Uniform Symmetrical Sampling Digital PWM for Power Converter." IEEE Transactions on Industrial Electronics 54, no. 1 (2007): 427–32. http://dx.doi.org/10.1109/tie.2006.885151.

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