Relevant bibliographies by topics / Paired

Academic literature on the topic 'Paired'

Author: Grafiati
Published: 4 June 2021
Last updated: 20 February 2023

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Journal articles on the topic "Paired"

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Malý, Martin, Kay Diederichs, Jan Dohnálek, and Petr Kolenko. "PAIREF: paired refinement also for Phenix users." Acta Crystallographica Section F Structural Biology Communications 77, no. 7 (June 29, 2021): 226–29. http://dx.doi.org/10.1107/s2053230x21006129.

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In macromolecular crystallography, paired refinement is generally accepted to be the optimal approach for the determination of the high-resolution cutoff. The software tool PAIREF provides automation of the protocol and associated analysis. Support for phenix.refine as a refinement engine has recently been implemented in the program. This feature is presented here using previously published data for thermolysin. The results demonstrate the importance of the complete cross-validation procedure to obtain a thorough and unbiased insight into the quality of high-resolution data.
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Malý, Martin, Kay Diederichs, Jan Dohnálek, and Petr Kolenko. "Paired refinement under the control of PAIREF." IUCrJ 7, no. 4 (June 10, 2020): 681–92. http://dx.doi.org/10.1107/s2052252520005916.

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Crystallographic resolution is a key characteristic of diffraction data and represents one of the first decisions an experimenter has to make in data evaluation. Conservative approaches to the high-resolution cutoff determination are based on a number of criteria applied to the processed X-ray diffraction data only. However, high-resolution data that are weaker than arbitrary cutoffs can still result in the improvement of electron-density maps and refined structure models. Therefore, the impact of reflections from resolution shells higher than those previously used in conservative structure refinement should be analysed by the paired refinement protocol. For this purpose, a tool called PAIREF was developed to provide automation of this protocol. As a new feature, a complete cross-validation procedure has also been implemented. Here, the design, usage and control of the program are described, and its application is demonstrated on six data sets. The results prove that the inclusion of high-resolution data beyond the conventional criteria can lead to more accurate structure models.
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Fitzpatrick, Shannon L., and Bert L. Hartnell. "Paired-domination." Discussiones Mathematicae Graph Theory 18, no. 1 (1998): 63. http://dx.doi.org/10.7151/dmgt.1063.

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Yarrow, Thomas. "Paired Opposites." Critique of Anthropology 28, no. 4 (December 2008): 426–45. http://dx.doi.org/10.1177/0308275x08098260.

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Hewson, Jan. "Paired spelling." Support for Learning 5, no. 3 (August 1990): 136–40. http://dx.doi.org/10.1111/j.1467-9604.1990.tb00402.x.

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Sutcliffe, Chris. "PAIRED READING." Early Years 6, no. 2 (March 1986): 31–37. http://dx.doi.org/10.1080/0957514860060205.

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Winter, Sam. "Paired Reading." Educational Psychology in Practice 12, no. 3 (October 1996): 182–90. http://dx.doi.org/10.1080/0266736960120308.

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Stammers, Trevor. "Paired papers." New Bioethics 24, no. 2 (May 4, 2018): 105. http://dx.doi.org/10.1080/20502877.2018.1477102.

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Robertson, Bruce. "Paired Perspectives." American Art 23, no. 3 (September 2009): 82–90. http://dx.doi.org/10.1086/649777.

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Askadskii, A. A., and I. I. Vointseva. "Paired polymers." Polymer Science U.S.S.R. 29, no. 12 (January 1987): 2922–38. http://dx.doi.org/10.1016/0032-3950(87)90216-4.

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Dissertations / Theses on the topic "Paired"

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Joscelyne, Trish. "Peer-tutored paired reading." Thesis, University of Sussex, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.332012.

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Okamoto, Heidi. "Stimulus preference assessment methods with preschool children: Computer paired stimulus vs. traditional paired stimulus." Scholarly Commons, 2005. https://scholarlycommons.pacific.edu/uop_etds/2664.

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Identifying potential reinforcers is an integral part to many behavior modification plans. The purpose of this study was to compare and contrast two methods of stimulus preference assessment (paired stimulus computer and a traditional paired stimulus with tangible and symbolic items) using typically developing preschool children. The participants were four 3-5 year old children recruited from a childcare facility in Stockton, CA. Both methods of stimulus preference assessment were administered to them and reinforcer assessments were conducted. Kendall Rank Correlations between the two methods were low. Both methods produced hierarchies of preference. Both methods produced a reinforcement effect and the difference between the magnitude of reinforcement for the computer and the traditional method was only statistically significant with one child (favoring the computer). The computer method was significantly more time efficient than the traditional method.
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Marco, Aguilar Ines. "Six Houses, Paired 2-2." Thesis, Högskolan i Halmstad, Sektionen för ekonomi och teknik (SET), 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-14983.

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A construction project including planning, cost estimation and design of ”Six Houses, Paired 2-2” on a property in the city of Halmstad.
Ett byggnadsprojekt med planering, kostnadsberäkning och projektering av ”Six Houses, Paired 2-2” på en fastighet i Halmstad.
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Sjölander, Morné Rowan. "SL-model for paired comparisons." Thesis, Nelson Mandela Metropolitan University, 2006. http://hdl.handle.net/10948/605.

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The method of paired comparisons can be found all the way back to 1860, where Fechner made the first publication in this method, using it for his psychometric investigations [4]. Thurstone formalised the method by providing a mathematical background to it [9-11] and in 1927 the method’s birth took place with his psychometric publications, one being “a law of comparative judgment” [12-14]. The law of comparative judgment is a set of equations relating the proportion of times any stimulus k is judged greater on a given attribute than any other stimulus j to the scales and discriminal dispersions of the two stimuli on the psychological continuum. The amount of research done for discrete models of paired comparisons is not a lot. This study develops a new discrete model, the SL-model for paired comparisons. Paired comparisons data processing in which objects have an upper limit to their scores was also not yet developed, and making such a model is one of the aims of this report. The SLmodel is thus developed in this context; however, the model easily generalises to not necessarily having an upper limit on scores.
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Proffitt, Kenneth Eugene. "Paired-domination in grid graphs." [Johnson City, Tenn. : East Tennessee State University], 2001. http://etd-submit.etsu.edu/etd/theses/available/etd-0213101-214504/restricted/proffittk.pdf.

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Sjolander, Morne Rowan. "Time series models for paired comparisons." Thesis, Nelson Mandela Metropolitan University, 2011. http://hdl.handle.net/10948/d1012858.

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The method of paired comparisons is seen as a technique used to rank a set of objects with respect to an abstract or immeasurable property. To do this, the objects get to be compared two at a time. The results are input into a model, resulting in numbers known as weights being assigned to the objects. The weights are then used to rank the objects. The method of paired comparisons was first used for psychometric investigations. Various other applications of the method are also present, for example economic applications, and applications in sports statistics. This study involves taking paired comparison models and making them time-dependent. Not much research has been done in this area. Three new time series models for paired comparisons are created. Simulations are done to support the evidence obtained, and theoretical as well as practical examples are given to illustrate the results and to verify the efficiency of the new models. A literature study is given on the method of paired comparisons, as well as on the areas in which we apply our models. Our first two time series models for paired comparisons are the Linear-Trend Bradley- Terry Model and the Sinusoidal Bradley-Terry Model. We use the maximum likelihood approach to solve these models. We test our models using exact and randomly simulated data for various time periods and various numbers of objects. We adapt the Linear-Trend Bradley-Terry Model and received our third time series model for paired comparisons, the Log Linear-Trend Bradley-Terry Model. The daily maximum and minimum temperatures were received for Port Elizabeth, Uitenhage and Coega for 2005 until 2009. To evaluate the performance of the Linear-Trend Bradley-Terry Model and the Sinusoidal Bradley-Terry Model on estimating missing temperature data, we artificially remove observations of temperature from Coega’s temperature dataset for 2006 until 2008, and use various forms of these models to estimate the missing data points. The exchange rates for 2005 until 2008 between the following currencies: the Rand, Dollar, Euro, Pound and Yen, were obtained and various forms of our Log Linear-Trend Bradley-Terry Model are used to forecast the exchange rate for one day ahead for each month in 2006 until 2008. One of the features of this study is that we apply our time series models for paired comparisons to areas which comprise non-standard paired comparisons; and we want to encourage the use of the method of paired comparisons in a broader sense than what it is traditionally used for. The results of this study can be used in various other areas, like for example, in sports statistics, to rank the strength of sports players and predict their future scores; in Physics, to calculate weather risks of electricity generation, particularly risks related to nuclear power plants, and so forth, as well as in many other areas. It is hoped that this research will open the door to much more research in combining time series analysis with the method of paired comparisons.
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Venter, Daniel Jacobus Lodewyk. "An evaluation of paired comparison models." Thesis, University of Port Elizabeth, 2004. http://hdl.handle.net/10948/364.

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Introduction: A typical task in quantitative data analysis is to derive estimates of population parameters based on sample statistics. For manifest variables this is usually a straightforward process utilising suitable measurement instruments and standard statistics such the mean, median and standard deviation. Latent variables on the other hand are typically more elusive, making it difficult to obtain valid and reliable measurements. One of the most widely used methods of estimating the parameter value of a latent variable is to use a summated score derived from a set of individual scores for each of the various attributes of the latent variable. A serious limitation of this method and other similar methods is that the validity and reliability of measurements depend on whether the statements included in the questionnaire cover all characteristics of the variable being measured and also on respondents’ ability to correctly indicate their perceived assessment of the characteristics on the scale provided. Methods without this limitation and that are especially useful where a set of objects/entities must be ranked based on the parameter values of one or more latent variables, are methods of paired comparisons. Although the underlying assumptions and algorithms of these methods often differ dramatically, they all rely on data derived from a series of comparisons, each consisting of a pair of specimens selected from the set of objects/entities being investigated. Typical examples of the comparison process are: subjects (judges) who have to indicate for each pair of objects which of the two they prefer; sport teams that compete against each other in matches that involve two teams at a time. The resultant data of each comparison range from a simple dichotomy to indicate which of the two objects are preferred/better, to an interval or ratio scale score for e d Bradley-Terry models, and were based on statistical theory assuming that the variable(s) being measured is either normally (Thurstone-Mosteller) or exponentially (Bradley-Terry) distributed. For many years researchers had to rely on these PCM’s when analysing paired comparison data without any idea about the implications if the distribution of the data from which their sample were obtained differed from the assumed distribution for the applicable PCM being utilised. To address this problem, PCM’s were subsequently developed to cater for discrete variables and variables with distributions that are neither normal or exponential. A question that remained unanswered is how the performance, as measured by the accuracy of parameter estimates, of PCM's are affected if they are applied to data from a range of discrete and continuous distribution that violates the assumptions on which the applicable paired comparison algorithm is based. This study is an attempt to answer this question by applying the most popular PCM's to a range of randomly derived data sets that spans typical continuous and discrete data distributions. It is hoped that the results of this study will assist researchers when selecting the most appropriate PCM to obtain accurate estimates of the parameters of the variables in their data sets.
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Dittrich, Regina, Brian Francis, and Walter Katzenbeisser. "Temporal dependence in longitudinal paired comparisons." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 2008. http://epub.wu.ac.at/1452/1/document.pdf.

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This paper develops a new approach to the analysis of longitudinal paired comparison data, where comparisons of the same objects by the same judges are made on more than one occasion. As an alternative to other recent approaches to such data, which are based on Kalman filter- ing, our approach treats the problem as one of multivariate multinomial data, allowing dependence terms between comparisons over time to be incorporated. The resulting model can be fitted as a Poisson log-linear model and has parallels with the quadratic binary exponential distribution of Cox. An example from the British Household Panel Survey illustrates the approach. (author´s abstract)
Series: Research Report Series / Department of Statistics and Mathematics
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Hatzinger, Reinhold, and Brian Francis. "Fitting paired comparison models in R." Institut für Statistik und Mathematik, WU Vienna University of Economics and Business, 2004. http://epub.wu.ac.at/740/1/document.pdf.

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Paired comparison models in loglinear form are generalised linear models and can be fitted using the IWLS algorithm. Unfortunately, the design matrices can become very large and thus a method is needed to reduce computational load (relating to both space and time). This paper discusses an algorithm for fitting loglinear paired comparison models in the presence of many nuisance parameters which is based on partition rules for symmetric matrices and takes advantage of the special structure of the design matrix in Poisson loglinear models. The algorithm is implemented as an R function. Some simple examples illustrate its use for fitting both paired comparison models and (multinomial) logit models. (author's abstract)
Series: Research Report Series / Department of Statistics and Mathematics
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Ramírez-Robredo, J. Antonio (José Antonio) 1976. "Paired simulation of I/O automata." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/86831.

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Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.
Includes bibliographical references (leaves 89-90).
by J. Antonio Ramírez-Robredo.
M.Eng.
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Books on the topic "Paired"

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Logue, Joyce Elizabeth. Paired reading: An evaluation. [S.l: The author], 1995.

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The method of paired comparisons. 2nd ed. London: C. Griffin, 1988.

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Nimmo, Sally. Successful partnership in paired reading. London: UEL, 1993.

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E. E. M. van Berkum. Optimal paired comparison designs for factorial experiments. Amsterdam: Matematisch Centrum, 1987.

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Kyllonen, Patrick C. Knowledge and processing speed as determinants of associative learning. Brooks Air Force Base, Tex: Air Force Human Resources Laboratory, Air Force Systems Command, 1989.

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Hastie, Trevor. Classification by pairwise coupling. Toronto: University of Toronto, 1996.

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Judi, Bamford, ed. Parental involvement and peer tutoring in mathematics and science: Developing paired maths into paired science. London: D. Fulton Publishers, 1998.

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Williams, Cindy. Analysis of subjective judgment matrices. Santa Monica, CA: Rand, 1985.

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Csépes, Ildikó. Measuring oral proficiency through paired-task performance. Frankfurt am Main: Peter Lang, 2009.

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Csépes, Ildikó. Measuring oral proficiency through paired-task performance. Frankfurt am Main: Peter Lang, 2009.

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Book chapters on the topic "Paired"

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Saville, David J., and Graham R. Wood. "Paired Samples." In Statistical Methods, 10–38. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4612-0747-4_2.

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Mehlhorn, Heinz. "Paired Organelles." In Encyclopedia of Parasitology, 2027. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-43978-4_4148.

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Mehlhorn, Heinz. "Paired Organelles." In Encyclopedia of Parasitology, 1. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-27769-6_4148-1.

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Weik, Martin H. "paired cable." In Computer Science and Communications Dictionary, 1220. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_13531.

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Weik, Martin H. "paired data." In Computer Science and Communications Dictionary, 1220. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_13532.

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Aust, Nicola, and Axel Kirste. "Paired Electrosynthesis." In Encyclopedia of Applied Electrochemistry, 1505–10. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4419-6996-5_370.

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Pizzarelli, F. "Paired Hemodiafiltration." In Hemodiafiltration, 131–37. Basel: KARGER, 2007. http://dx.doi.org/10.1159/000107243.

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Larner, A. J. "Paired Measures." In The 2x2 Matrix, 15–47. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74920-0_2.

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Mishra, Raju Kumar. "The Power of Pairs: Paired RDDs." In PySpark Recipes, 115–36. Berkeley, CA: Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-3141-8_5.

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Cleophas, Ton J., and Aeilko H. Zwinderman. "Paired Continuous Data (Paired-t, Wilcoxon) (10 Patients)." In SPSS for Starters, 7–9. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-9519-0_3.

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Conference papers on the topic "Paired"

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Chao, Linlin, Jianshan He, Taifeng Wang, and Wei Chu. "PairRE: Knowledge Graph Embeddings via Paired Relation Vectors." In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.acl-long.336.

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Pharr, Walter M., Christopher W. Starr, and Caroline S. Starr. "Paired professional development." In the 14th annual ACM SIGCSE conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1562877.1562947.

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Dalal, E. N., J. C. Handley, W. Wu, and J. Wang. "Anchored paired comparisons." In Electronic Imaging 2008, edited by Susan P. Farnand and Frans Gaykema. SPIE, 2008. http://dx.doi.org/10.1117/12.766363.

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ZHukenov, D. Z. "About paired primes." In Scientific achievements of the third millennium. LJournal, 2019. http://dx.doi.org/10.18411/scienceconf-09-2019-16.

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Kuang, Zhanghui, and Kenneth Wong. "Relatively-Paired Space Analysis." In British Machine Vision Conference 2013. British Machine Vision Association, 2013. http://dx.doi.org/10.5244/c.27.131.

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Hunka, Frantisek, and Jiří Matula. "Towards Paired Transactions Modeling." In 2016 Federated Conference on Computer Science and Information Systems. IEEE, 2016. http://dx.doi.org/10.15439/2016f145.

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Nicolae, Ileana-Diana, and Petre-Marian Nicolae. "Practical aspects related to paired nodes and paired harmonics in WPT analysis." In IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2016. http://dx.doi.org/10.1109/iecon.2016.7793487.

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Leiden, Kenneth, Mary Ellen Miller, Curt Kaler, Jon Cunningham, Rick Berckefeldt, Bill True, Jason McMahon, et al. "Paired approach flight demonstration results." In 2019 Integrated Communications, Navigation and Surveillance Conference (ICNS). IEEE, 2019. http://dx.doi.org/10.1109/icnsurv.2019.8735116.

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"Paired Approach Flight Demonstration Results." In 2019 Integrated Communications, Navigation and Surveillance Conference (ICNS). IEEE, 2019. http://dx.doi.org/10.1109/icnsurv.2019.8735207.

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Bach, Stephen H., and Marcus A. Maloof. "Paired Learners for Concept Drift." In 2008 Eighth IEEE International Conference on Data Mining (ICDM). IEEE, 2008. http://dx.doi.org/10.1109/icdm.2008.119.

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Reports on the topic "Paired"

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Thompson, Andrew A. Interval Scales From Paired Comparisons. Fort Belvoir, VA: Defense Technical Information Center, May 2012. http://dx.doi.org/10.21236/ada568737.

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Lu, Wei-Kao, and Paul Debski. Paired Straight Hearth Furnace - Transformational Ironmaking Process. Office of Scientific and Technical Information (OSTI), November 2014. http://dx.doi.org/10.2172/1340663.

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Glickman, Mark E. Paired Comparison Models with Time-Varying Parameters. Fort Belvoir, VA: Defense Technical Information Center, May 1993. http://dx.doi.org/10.21236/ada272016.

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Stern, Hal. Are all Linear Paired Comparison Models Equivalent. Fort Belvoir, VA: Defense Technical Information Center, September 1990. http://dx.doi.org/10.21236/ada236856.

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Gezerlis, Alexandros, and Joseph A. Carlson. Strongly-paired fermions: cold atoms and neutron matter. Office of Scientific and Technical Information (OSTI), November 2007. http://dx.doi.org/10.2172/1454972.

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Gentry, Sommer. Towards an Online Matching Mechanism for Kidney Paired Donation. Fort Belvoir, VA: Defense Technical Information Center, January 2007. http://dx.doi.org/10.21236/ada519470.

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Peng, Ze, Matthew Hamilton, Jeff Froula, Aren Ewing, Brian Foster, and Jan-Fang Cheng. Large Gap Size Paired-end Library Construction for Second Generation Sequencing. Office of Scientific and Technical Information (OSTI), May 2010. http://dx.doi.org/10.2172/985369.

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Frimenko, Rebecca, Charles Goodyear, and Dustin Bruening. Changes in Spatiotemporal Differences Between the Sexes due to Paired Walking. Fort Belvoir, VA: Defense Technical Information Center, June 2016. http://dx.doi.org/10.21236/ada637020.

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Conley, Andrea, Brendan Donohoe, and Benjamin Greene. Aftershock Identification Using a Paired Neural Network Applied to Constructed Data. Office of Scientific and Technical Information (OSTI), June 2021. http://dx.doi.org/10.2172/1821802.

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Allmen, M., and W. Kegelmeyer, Jr. The computation of cloud base height from paired whole-sky imaging cameras. Office of Scientific and Technical Information (OSTI), March 1994. http://dx.doi.org/10.2172/10141848.

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