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Relevant bibliographies by topics / Sampling effects

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Academic literature on the topic 'Sampling effects'

Author: Grafiati

Published: 21 December 2024

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Journal articles on the topic "Sampling effects"

1

Johnson, Norman L., Samuel Kotz, and Robert N. Rodriguez. "Statistical Effects of Imperfect Inspection Sampling: II. Double Sampling and Link Sampling." Journal of Quality Technology 18, no. 2 (April 1986): 116–38. http://dx.doi.org/10.1080/00224065.1986.11978996.

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Wade, Angie. "Matched Sampling for Causal Effects." Journal of the Royal Statistical Society: Series A (Statistics in Society) 171, no. 3 (June 2008): 760–61. http://dx.doi.org/10.1111/j.1467-985x.2008.00538_9.x.

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Scheiner, Samuel M. "Affinity analysis: effects of sampling." Vegetatio 86, no. 2 (April 1990): 175–81. http://dx.doi.org/10.1007/bf00031733.

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Snoeijs, P. J. M. "Monitoring pollution effects by diatom community composition. A comparison of sampling methods." Archiv für Hydrobiologie 121, no. 4 (August 13, 1991): 497–510. http://dx.doi.org/10.1127/archiv-hydrobiol/121/1991/497.

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Royle, J. Andrew, Deanna K. Dawson, and Scott Bates. "MODELING ABUNDANCE EFFECTS IN DISTANCE SAMPLING." Ecology 85, no. 6 (June 2004): 1591–97. http://dx.doi.org/10.1890/03-3127.

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Barcelona, Michael J., John A. Helfrich, and Edward E. Garske. "Sampling Tubing Effects on Groundwater Samples." Analytical Chemistry 57, no. 2 (February 1985): 460–64. http://dx.doi.org/10.1021/ac50001a032.

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OKAMOTO, Yasuharu. "Effects of Biases in Snowball Sampling." Proceedings of the Annual Convention of the Japanese Psychological Association 76 (September 11, 2012): 1EVA20. http://dx.doi.org/10.4992/pacjpa.76.0_1eva20.

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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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Lauzon, M. Louis, and Brian K. Rutt. "Effects of polar sampling ink-space." Magnetic Resonance in Medicine 36, no. 6 (December 1996): 940–49. http://dx.doi.org/10.1002/mrm.1910360617.

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Kim, Taehoon, KeeJae Lee, and Inho Park. "Measuring stratification effects for multistage sampling." Korean Journal of Applied Statistics 36, no. 4 (August 31, 2023): 337–47. http://dx.doi.org/10.5351/kjas.2023.36.4.337.

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Dissertations / Theses on the topic "Sampling effects"

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Hopper, R. J. "The effects and implications of sampling clay soils." Thesis, University of Surrey, 1992. http://epubs.surrey.ac.uk/804887/.

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Hicks, William T. "Analysis of the Effects of Sampling Sampled Data." International Foundation for Telemetering, 1996. http://hdl.handle.net/10150/611440.

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International Telemetering Conference Proceedings / October 28-31, 1996 / Town and Country Hotel and Convention Center, San Diego, California
The traditional use of active RC-type filters as anti-aliasing filters in Pulse Code Modulation (PCM) systems is being replaced by the use of Digital Signal Processing (DSP) filters, especially when performance requirements are tight and when operation over a wide environmental temperature range is required. In order to keep systems more flexible, it is often desired to let the DSP filters run asynchronous to the PCM sample clock. This results in the PCM output signal being a sampling of the output of the DSP, which is itself a sampling of the input signal. In the analysis of the PCM data, the signal will have a periodic repeat of a previous sample, or a missing sample, depending on the relative sampling rates of the DSP and the PCM. This paper analyzes what effects can be expected in the analysis of the PCM data when these anomalies are present. Results are presented which allow the telemetry engineer to make an effective value judgment based on the type of filtering technology to be employed and on the desired system performance.

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Tse, Kwok Ho. "Sample size calculation : influence of confounding and interaction effects /." View abstract or full-text, 2006. http://library.ust.hk/cgi/db/thesis.pl?MATH%202006%20TSE.

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Smith, Corey B. "The effects of chorionic villus sampling upon marital adjustment." Theological Research Exchange Network (TREN), 1994. http://www.tren.com.

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Prevost, Andrew Toby. "Multilevel modelling of child mortality : Gibbs sampling versus other approaches." Thesis, University of Southampton, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.242478.

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Chen, Liang. "Small population bias and sampling effects in stochastic mortality modelling." Thesis, Heriot-Watt University, 2017. http://hdl.handle.net/10399/3372.

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Pension schemes are facing more difficulties on matching their underlying liabilities with assets, mainly due to faster mortality improvements for their underlying populations, better environments and medical treatments and historically low interest rates. Given most of the pension schemes are relatively much smaller than the national population, modelling and forecasting the small populations' longevity risk become urgent tasks for both the industrial practitioners and academic researchers. This thesis starts with a systematic analysis on the influence of population size on the uncertainties of mortality estimates and forecasts with a stochastic mortality model, based on a parametric bootstrap methodology with England and Wales males as our benchmark population. The population size has significant effect on the uncertainty of mortality estimates and forecasts. The volatilities of small populations are over-estimated by the maximum likelihood estimators. A Bayesian model is developed to improve the estimation of the volatilities and the predictions of mortality rates for the small populations by employing the information of larger population with informative prior distributions. The new model is validated with the simulated small death scenarios. The Bayesian methodologies generate smoothed estimations for the mortality rates. Moreover, a methodology is introduced to use the information of large population for obtaining unbiased volatilities estimations given the underlying prior settings. At last, an empirical study is carried out based on the Scotland mortality dataset.

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Smith, Christina D. "Estimation of treatment effects under combined sampling and experimental designs." Diss., Manhattan, Kan. : Kansas State University, 2007. http://hdl.handle.net/2097/282.

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Jia, Yue. "Using sampling weights in the estimation of random effects model." Ann Arbor, Mich. : ProQuest, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3258527.

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Thesis (Ph.D. in Statistical Science)--S.M.U., 2007.
Title from PDF title page (viewed Mar. 18, 2008). Source: Dissertation Abstracts International, Volume: 68-04, Section: B, page: 2431. Adviser: Lynne Stokes. Includes bibliographical references.

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Tansey, Joshua. "The effects of clumped log distribution on line intersect sampling." Thesis, University of Canterbury. School of Forestry, 2014. http://hdl.handle.net/10092/10463.

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Line intersect sampling (LIS) is a method used for quantifying post-harvest waste. It is often used by forest managers to quantify merchantable volume remaining on the cutover so that compensation may be exacted under stumpage contracts. The theory has been extensively studied and will produce an accurate measure of harvest waste given the basic theoretical assumptions that: all logs are cylindrical, occur horizontally, are randomly orientated and randomly distributed. When these assumptions are violated, the method is not biased, although precision decreases substantially. A computer simulation was completed to determine whether or not the LIS method is appropriate, given a clumped distribution of logs produced by processing at central sites in cutover before using a forwarder to extract to the landing. The software ArcGIS with the application ModelBuilder was used to produce the LIS Model for running LIS assessments. It was determined through simulation that the conventional LIS method is not appropriate given these harvesting methods, as a level of bias was found in sampling determining that the LIS method underestimated true volume. T-tests confirmed the significance of this bias. LIS volume estimates were not precise, with the range of estimates ranging from 0 m3/ha to double the true volume. An increase in sampling length by a third was found to increase precision by only a small amount. Therefore, it was determine that increased sampling is not worthwhile as the costs associated with it do not justify the small increase in precision.

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Miracle, Sarah. "The effects of bias on sampling algorithms and combinatorial objects." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53526.

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Markov chains are algorithms that can provide critical information from exponentially large sets efficiently through random sampling. These algorithms are ubiquitous across numerous scientific and engineering disciplines, including statistical physics, biology and operations research. In this thesis we solve sampling problems at the interface of theoretical computer science with applied computer science, discrete mathematics, statistical physics, chemistry and economics. A common theme throughout each of these problems is the use of bias. The first problem we study is biased permutations which arise in the context of self-organizing lists. Here we are interested in the mixing time of a Markov chain that performs nearest neighbor transpositions in the non-uniform setting. We are given "positively biased'' probabilities $\{p_{i,j} \geq 1/2 \}$ for all $i < j$ and let $p_{j,i} = 1-p_{i,j}$. In each step, the chain chooses two adjacent elements~$k,$ and~$\ell$ and exchanges their positions with probability $p_{ \ell, k}$. We define two general classes of bias and give the first proofs that the chain is rapidly mixing for both. We also demonstrate that the chain is not always rapidly mixing by constructing an example requiring exponential time to converge to equilibrium. Next we study rectangular dissections of an $n \times n$ lattice region into rectangles of area $n$, where $n=2^k$ for an even integer $k.$ We consider a weighted version of a natural edge flipping Markov chain where, given a parameter $\lambda > 0,$ we would like to generate each rectangular dissection (or dyadic tiling)~$\sigma$ with probability proportional to $\lambda^{|\sigma|},$ where $|\sigma|$ is the total edge length. First we look at the restricted case of dyadic tilings, where each rectangle is required to have the form $R = [s2^{u},(s+1)2^{u}]\times [t2^{v},(t+1)2^{v}],$ where $s, t, u$ and~$v$ are nonnegative integers. Here we show there is a phase transition: when $\lambda < 1,$ the edge-flipping chain mixes in time $O(n^2 \log n)$, and when $\lambda > 1,$ the mixing time is $\exp(\Omega({n^2}))$. The behavior for general rectangular dissections is more subtle, and we show the chain requires exponential time when $\lambda >1$ and when $\lambda <1.$ The last two problems we study arise directly from applications in chemistry and economics. Colloids are binary mixtures of molecules with one type of molecule suspended in another. It is believed that at low density typical configurations will be well-mixed throughout, while at high density they will separate into clusters. We characterize the high and low density phases for a general family of discrete interfering colloid models by showing that they exhibit a "clustering property" at high density and not at low density. The clustering property states that there will be a region that has very high area to perimeter ratio and very high density of one type of molecule. A special case is mixtures of squares and diamonds on $\Z^2$ which correspond to the Ising model at fixed magnetization. Subsequently, we expanded techniques developed in the context of colloids to give a new rigorous underpinning to the Schelling model, which was proposed in 1971 by economist Thomas Schelling to understand the causes of racial segregation. Schelling considered residents of two types, where everyone prefers that the majority of his or her neighbors are of the same type. He showed through simulations that even mild preferences of this type can lead to segregation if residents move whenever they are not happy with their local environments. We generalize the Schelling model to include a broad class of bias functions determining individuals happiness or desire to move. We show that for any influence function in this class, the dynamics will be rapidly mixing and cities will be integrated if the racial bias is sufficiently low. However when the bias is sufficiently high, we show the dynamics take exponential time to mix and a large cluster of one type will form.

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Books on the topic "Sampling effects"

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Lesage, J., I. DeGraff, and R. Danchik, eds. Isocyanates: Sampling, Analysis, and Health Effects. 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959: ASTM International, 2001. http://dx.doi.org/10.1520/stp1408-eb.

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1958-, Lesage Jacques, DeGraff Irene 1948-, and Danchik Richard 1943-, eds. Isocyanates: Sampling, analysis, and health effects. W. Conshohocken, PA: ASTM, 2001.

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Ranney, Christine K. Sample partitioning bias in estimating the effects of the Food Stamp Program. Ithaca, N.Y: Dept. of Agricultural Economics, Cornell University Agricultural Experiment Station, New York State College of Agriculture and Life Sciences, 1985.

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Research, American Institutes for, and National Center for Education Statistics., eds. The effects of finite sampling corrections on state assessment sample requirements. Palo Alto, CA: American Institutes for Research, 1998.

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Harris, Alfred Ray. Using column lysimetry to evaluate acid precipitation effects. St. Paul, Minn: U.S. Dept. of Agriculture, Forest Service, North Central Forest Experiment Station, 1990.

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Harris, Alfred Ray. Using column lysimetry to evaluate acid precipitation effects. St. Paul, Minn: U.S. Dept. of Agriculture, Forest Service, North Central Forest Experiment Station, 1990.

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Center, Langley Research, ed. Sampling and position effects in the Electronically Steered Thinned Array Radiometer (ESTAR). Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1993.

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Billings, S. A. Effects of the sampling time on the dynamics and identification of nonlinear models. Sheffield: University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1994.

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Flannagan, J. F. Studies on some riverine insect emergence traps: Effects of sampling frequency and trap design. Winnipeg, Man: Central and Arctic Region, Dept. of Fisheries and Oceans, 1994.

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D, Higgins Jerry, United States. Dept. of Energy. Nevada Operations Office, and Geological Survey (U.S.), eds. The one-dimensional compression method for extraction of pore water from unsaturated tuff and effects on pore-water chemistry. Denver, Colo: U.S. Dept. of the Interior, U.S. Geological Survey, 1997.

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Book chapters on the topic "Sampling effects"

1

Schroeder, Douglas A. "Repeated-sampling inference." In Accounting and Causal Effects, 107–22. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-7225-5_7.

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Goldstein, Harvey. "Sampling for Growth Studies." In Methodology Ecological, Genetic, and Nutritional Effects on Growth, 59–78. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4615-7198-8_3.

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Laaksonen, Seppo. "Design Effects at the Sampling Phase." In Survey Methodology and Missing Data, 77–89. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-79011-4_5.

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Grannis, Rick. "Sampling Effects in Social Network Analysis." In Encyclopedia of Social Network Analysis and Mining, 1–11. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4614-7163-9_37-1.

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Grannis, Rick. "Sampling Effects in Social Network Analysis." In Encyclopedia of Social Network Analysis and Mining, 1607–16. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-6170-8_37.

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Grannis, Rick. "Sampling Effects in Social Network Analysis." In Encyclopedia of Social Network Analysis and Mining, 2281–90. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7131-2_37.

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Bažant, Zdeněk P., and Milan Jirásek. "Uncertainty Due to Parameter Randomness via Sampling of Deterministic Solutions." In Creep and Hygrothermal Effects in Concrete Structures, 177–204. Dordrecht: Springer Netherlands, 2018. http://dx.doi.org/10.1007/978-94-024-1138-6_6.

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Clarke, Ralph T., Armin Lorenz, Leonard Sandin, Astrid Schmidt-Kloiber, Joerg Strackbein, Nick T. Kneebone, and Peter Haase. "Effects of sampling and sub-sampling variation using the STAR-AQEM sampling protocol on the precision of macroinvertebrate metrics." In The Ecological Status of European Rivers: Evaluation and Intercalibration of Assessment Methods, 441–59. Dordrecht: Springer Netherlands, 2006. http://dx.doi.org/10.1007/978-1-4020-5493-8_31.

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Gessing, Ryszard. "Finite Word-Length Effects in Systems with Fast Sampling." In Digital Controller Implementation and Fragility, 25–42. London: Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-0265-6_3.

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Banning, E. B. "Units, Sampling Frames, and Edge Effects in Archaeological Survey." In Archaeological Survey, 75–112. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0769-7_4.

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Conference papers on the topic "Sampling effects"

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Powell, Henry, Robert W. Jackson, and Do-Hoon Kwon. "Effects of Jammer Bandwidth and Sampling Duration on CRPA Null Placement." In 2024 IEEE INC-USNC-URSI Radio Science Meeting (Joint with AP-S Symposium), 143–44. IEEE, 2024. http://dx.doi.org/10.23919/inc-usnc-ursi61303.2024.10632472.

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Raj, J., M. Millardet, M. Gao, J. S. Karp, M. D. Witherspoon, and S. Matej. "Effects of Angular Sampling in Deep-Learning based PET Reconstruction for Total-Body PET." In 2024 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD), 1. IEEE, 2024. http://dx.doi.org/10.1109/nss/mic/rtsd57108.2024.10658576.

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Mann, David C. "Pixel Sampling Effects in Hyperspectral Imagers." In Imaging Systems and Applications. Washington, D.C.: OSA, 2020. http://dx.doi.org/10.1364/isa.2020.itu4g.5.

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Cook, Thomas H., Charles S. Hall, Frederick G. Smith, and Timothy J. Rogne. "Simulation of sampling effects in FPAs." In Orlando '91, Orlando, FL, edited by Gerald C. Holst. SPIE, 1991. http://dx.doi.org/10.1117/12.45804.

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Borys, Andrzej, and Przemyslaw Korohoda. "Analysis of critical sampling effects revisited." In 2017 Signal Processing: Algorithms, Architectures, Arrangements and Applications (SPA). IEEE, 2017. http://dx.doi.org/10.23919/spa.2017.8166852.

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Bradley, D. J., P. N. J. Dennis, Lionel R. Baker, and Andre Masson. "Sampling effects in CdHgTe focal plane arrays." In 1985 International Technical Symposium/Europe. SPIE, 1986. http://dx.doi.org/10.1117/12.951965.

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Beechner, Troy, and Jian Sun. "Optimal interleaved pulsewidth modulation considering sampling effects." In 2011 IEEE Applied Power Electronics Conference and Exposition - APEC 2011. IEEE, 2011. http://dx.doi.org/10.1109/apec.2011.5744852.

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Arai, Miho, Isao Shimizu, Haruo Kobayashi, Keita Kurihara, Shu Sasaki, Shohei Shibuya, Kiichi Niitsu, and Kazuyoshi Kubo. "Finite aperture time effects in sampling circuit." In 2015 IEEE 11th International Conference on ASIC (ASICON ). IEEE, 2015. http://dx.doi.org/10.1109/asicon.2015.7516913.

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Chen, Kuan-Chun, and Shang-Ho Tsai. "Reducing jitter effects for multiband bandpass sampling systems." In 2015 IEEE International Conference on Digital Signal Processing (DSP). IEEE, 2015. http://dx.doi.org/10.1109/icdsp.2015.7251897.

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Kallinger, Markus, Michael Buerger, Oliver Thiergart, Fabian Kuech, and Dirk Mahne. "Resolving spatial sampling effects in parametric directional filtering." In ICASSP 2011 - 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2011. http://dx.doi.org/10.1109/icassp.2011.5946346.

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Reports on the topic "Sampling effects"

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Ait-Sahalia, Yacine, and Per Mykland. The Effects of Random and Discrete Sampling When Estimating Continuous-Time Diffusions. Cambridge, MA: National Bureau of Economic Research, April 2002. http://dx.doi.org/10.3386/t0276.

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Kelner, Britton, and Sparks. L51986 Natural Gas Sample Collection and Handling Phase II Simulated Field Conditions. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), January 2003. http://dx.doi.org/10.55274/r0011157.

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Phase II was originally planned as a series of field tests to confirm the results of the sampling methods performance tests conducted during Phase I. However, the API Chapter 14.1 Gas Sampling Research Working Group chose to have the tests conducted at a newly developed wet gas test facility located at the Colorado Engineering Experiment Station (CEESI), in Nunn, Colorado. Three general tests were conducted. Test Plan I was intended to investigate the effects of sample point location on on-line gas chromatograph (GC) analyses and on spot sampling methods. Test Plan II was intended to investigate the effects of sample point location on on-line GC analyses and to compare several spot sampling methods when sampling from the same point. Test Plan III was intended to investigate the effects of coupling configurations and cylinder temperature on two specific methods: Helium Pop, and purging - Fill/Empty.

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Axelrod, M. Using Ancillary Information to Reduce Sample Size in Discovery Sampling and the Effects of Measurement Error. Office of Scientific and Technical Information (OSTI), August 2005. http://dx.doi.org/10.2172/877925.

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Johnson, Mark, Peter Tyack, Doug Gillespie, and Bernie McConnell. A Multi-Week Behavioral Sampling Tag for Sound Effects Studies: Design Trade-Offs and Prototype Evaluation. Fort Belvoir, VA: Defense Technical Information Center, September 2012. http://dx.doi.org/10.21236/ada573540.

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Grimley, Terry. PR-015-17610-Z01 Effects of Changing Gas Composition on Flow Measurement Error. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), May 2019. http://dx.doi.org/10.55274/r0011583.

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The influence of measurement errors in gas composition on the energy flow measurement was examined for flow meters based on differential pressure (e.g., orifice, Venturi, and cone), linear volumetric meters (e.g., turbine and ultrasonic), and mass flow meters (e.g., Coriolis). The purpose of this analysis was to develop a process that could be used to assess the requirements for gas composition accuracy and sampling rate to meet a given overall target accuracy in energy flow measurement. Includes associated spreadsheets. There is also a related webinar. ?

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Mattigod, Shas V., and Wayne J. Martin. Radionuclide Activities in Contaminated Soils: Effects of Sampling Bias on Remediation of Coarse-Grained Soils in Hanford Formation. Office of Scientific and Technical Information (OSTI), August 2001. http://dx.doi.org/10.2172/786811.

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Mattigod, Shas V., and Wayne J. Martin. Radionuclide Activities in Contaminated Soils: Effects of Sampling Bias on Remediation of Coarse-Grained Soils in Hanford Formation. Office of Scientific and Technical Information (OSTI), August 2001. http://dx.doi.org/10.2172/965707.

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Liikala, T. L., D. S. Daly, and A. P. Toste. An evaluation of the effects of well construction materials and ground-water sampling equipment on concentrations of volatile organic compounds. Office of Scientific and Technical Information (OSTI), August 1988. http://dx.doi.org/10.2172/6847584.

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Davidson, A., and S. Wang. The effects of sampling resolution on the surface albedos of dominant land cover types in the North American boreal region. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2004. http://dx.doi.org/10.4095/220099.

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George, Darin, and Christopher Grant. PR-015-15601-R01 Pulsation Effects on Ultrasonic Meters Phase II. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), April 2016. http://dx.doi.org/10.55274/r0010905.

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Transmission and storage operations frequently move natural gas using reciprocating compressors that may generate flow pulsations. Most measurement systems cannot accurately measure the flow rate of a pulsating gas stream, and the resulting errors can cause inaccurate gas volumes and accounting imbalances. A previous PRCI research project tested whether recent advances in ultrasonic meters may allow them to function without measurement error in pulsating flows. This project expanded on the previous work and tested similar ultrasonic meters to look for relationships between ultrasonic meter transducer sampling rates, the frequency and amplitudes of pulsations from reciprocating compressors, and meter accuracy. Diagnostics and flow data were collected from the meters and analyzed, and a useful relationship was found between the pulsation conditions and the meter measurement error. The findings were used to recommend a basis for installing ultrasonic meters in gas pipelines with varying pulsations. Additional testing evaluated a fast-response differential pressure transducer connected across a plate flow conditioner as a potential pulsation diagnostic tool.

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