Relevant bibliographies by topics / Statistical set

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Statistical set'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Statistical set"

1

Neill, Alex. "Developing statistical numeracy in primary schools." Set: Research Information for Teachers, no. 1 (May 1, 2012): 9–16. http://dx.doi.org/10.18296/set.0369.

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Sharma, Sashi, Phil Doyle, and Viney Shandil. "Developing statistical literacy with Year 9 students." Set: Research Information for Teachers, no. 1 (May 1, 2011): 43–50. http://dx.doi.org/10.18296/set.0398.

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Das, Pratulananda, Sanjoy Ghosal, Avishek Ghosh, and Sumit Som. "Characterization of rough weighted statistical limit set." Mathematica Slovaca 68, no. 4 (August 28, 2018): 881–96. http://dx.doi.org/10.1515/ms-2017-0152.

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Abstract Our focus is to generalize the definition of the weighted statistical convergence in a wider range of the weighted sequence {tn}n∈ℕ. We extend the concept of weighted statistical convergence and rough statistical convergence to renovate a new concept namely, rough weighted statistical convergence. On a continuation we also define rough weighted statistical limit set. In the year (2008) Aytar established the following results: The diameter of rough statistical limit set of a real sequence is ≤ 2r (where r is the degree of roughness) and in general it has no smaller bound. If the rough statistical limit set is non-empty then the sequence is statistically bounded. If x∗ and c belong to rough statistical limit set and statistical cluster point set respectively, then |x∗ − c| ≤ r. We investigate whether the above mentioned three results are satisfied for rough weighted statistical limit set or not? Answer is no. So our main objective is to interpret above mentioned different behaviors of the new convergence and characterize the rough weighted statistical limit set. Also we show that this set satisfies some topological properties like boundedness, compactness, path connectedness etc.
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Balog, Antal, and Endre Szemer�di. "A statistical theorem of set addition." Combinatorica 14, no. 3 (September 1994): 263–68. http://dx.doi.org/10.1007/bf01212974.

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Rouanet, Henry, Jean-Marc Bernard, and Bruno Lecoutre. "Nonprobabilistic Statistical Inference: A Set-Theoretic Approach." American Statistician 40, no. 1 (February 1986): 60. http://dx.doi.org/10.2307/2683134.

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Roberts, David W. "STATISTICAL ANALYSIS OF MULTIDIMENSIONAL FUZZY SET ORDINATIONS." Ecology 89, no. 5 (May 2008): 1246–60. http://dx.doi.org/10.1890/07-0136.1.

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de Fockert, Jan W., and Alexander P. Marchant. "Attention modulates set representation by statistical properties." Perception & Psychophysics 70, no. 5 (July 2008): 789–94. http://dx.doi.org/10.3758/pp.70.5.789.

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Hanebeck, Uwe D., Joachim Horn, and Günther Schmidt. "On combining statistical and set-theoretic estimation." Automatica 35, no. 6 (June 1999): 1101–9. http://dx.doi.org/10.1016/s0005-1098(99)00011-4.

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Rouanet, Henry, Jean-Marc Bernard, and Bruno Lecoutre. "Nonprobabilistic Statistical Inference: A Set-Theoretic Approach." American Statistician 40, no. 1 (February 1986): 60–65. http://dx.doi.org/10.1080/00031305.1986.10475358.

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Ulusu, Uğur, and Fatih Nuray. "On Asymptotically Lacunary Statistical Equivalent Set Sequences." Journal of Mathematics 2013 (2013): 1–5. http://dx.doi.org/10.1155/2013/310438.

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This paper presents three definitions which are natural combination of the definitions of asymptotic equivalence, statistical convergence, lacunary statistical convergence, and Wijsman convergence. In addition, we also present asymptotically equivalent (Wijsman sense) analogs of theorems in Patterson and Savaş (2006).
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Dissertations / Theses on the topic "Statistical set"

1

Marchant, Alexander. "Set representation by statistical properties." Thesis, Goldsmiths College (University of London), 2011. http://research.gold.ac.uk/6518/.

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This thesis has investigated the apparent ability of the visual system to represent a set of similar objects with a summary description instead of information about the individual items themselves (Ariely, 2001; Chong and Treisman, 2005a). Summary descriptions can be based on set sizes that are beyond the capacity of focussed attention, leading to the proposal that a distributed attention mechanism, statistical processing, underlies this process (Chong and Treisman, 2003, 2005a, 2005b; Chong et al. 2008; Treisman, 2006). However, the conclusion that summary descriptions are formed by a mechanism involving distributed attention has been questioned on the basis of parsimony, and a proposal for the role of focussed attention strategies in producing these summary descriptions has been made (Myzcek & Simons, 2008; Simons & Myzcek, 2008; see also De Fockert & Marchant, 2008). The aim of this thesis was to further elucidate the process of set representation by statistical properties, exploring the evidence that the summary description is given preferential representational status over individual items (Chapter 2), that summary descriptions can be produced within the known capacity limits of focussed attention (Chapter 3), that the results found in these experiments are not affected by the development of a prototypical average across the experimental session (Chapter 4), and that similar summary descriptions may also be rapidly extracted from more complex stimuli (Chapter 5). These findings are discussed in the context of current average size perception theory, and the proposal of a dual process view of set representation by statistical properties is briefly outlined. The dual process view combines both focussed attention when stimulus complexity is low and/or cognitive resources are available and distributed attention when stimulus complexity is high and/or cognitive resources are restricted. Finally, a selection of further studies and research areas that follow from the current research and the dual process view are briefly detailed.
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Crafford, Gretel. "Statistical analysis of grouped data." Thesis, University of Pretoria, 2007. http://hdl.handle.net/2263/25968.

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The maximum likelihood (ML) estimation procedure of Matthews and Crowther (1995: A maximum likelihood estimation procedure when modelling in terms of constraints. South African Statistical Journal, 29, 29-51) is utilized to fit a continuous distribution to a grouped data set. This grouped data set may be a single frequency distribution or various frequency distributions that arise from a cross classification of several factors in a multifactor design. It will also be shown how to fit a bivariate normal distribution to a two-way contingency table where the two underlying continuous variables are jointly normally distributed. This thesis is organized in three different parts, each playing a vital role in the explanation of analysing grouped data with the ML estimation of Matthews and Crowther. In Part I the ML estimation procedure of Matthews and Crowther is formulated. This procedure plays an integral role and is implemented in all three parts of the thesis. In Part I the exponential distribution is fitted to a grouped data set to explain the technique. Two different formulations of the constraints are employed in the ML estimation procedure and provide identical results. The justification of the method is further motivated by a simulation study. Similar to the exponential distribution, the estimation of the normal distribution is also explained in detail. Part I is summarized in Chapter 5 where a general method is outlined to fit continuous distributions to a grouped data set. Distributions such as the Weibull, the log-logistic and the Pareto distributions can be fitted very effectively by formulating the vector of constraints in terms of a linear model. In Part II it is explained how to model a grouped response variable in a multifactor design. This multifactor design arise from a cross classification of the various factors or independent variables to be analysed. The cross classification of the factors results in a total of T cells, each containing a frequency distribution. Distribution fitting is done simultaneously to each of the T cells of the multifactor design. Distribution fitting is also done under the additional constraints that the parameters of the underlying continuous distributions satisfy a certain structure or design. The effect of the factors on the grouped response variable may be evaluated from this fitted design. Applications of a single-factor and a two-factor model are considered to demonstrate the versatility of the technique. A two-way contingency table where the two variables have an underlying bivariate normal distribution is considered in Part III. The estimation of the bivariate normal distribution reveals the complete underlying continuous structure between the two variables. The ML estimate of the correlation coefficient ρ is used to great effect to describe the relationship between the two variables. Apart from an application a simulation study is also provided to support the method proposed.
Thesis (PhD (Mathematical Statistics))--University of Pretoria, 2007.
Statistics
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譚玉貞 and Yuk-ching Tam. "Some practical issues in estimation based on a ranked set sample." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1999. http://hub.hku.hk/bib/B31221683.

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Li, Tao Balakrishnan N. "Ordered ranked set samples and applications to statistical inference." *McMaster only, 2005.

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Frey, Jesse C. "Inference procedures based on order statistics." Connect to this title online, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1122565389.

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Thesis (Ph. D.)--Ohio State University, 2005.
Title from first page of PDF file. Document formatted into pages; contains xi, 148 p.; also includes graphics. Includes bibliographical references (p. 146-148). Available online via OhioLINK's ETD Center
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Cotellesso, Paul. "Statistical and Fuzzy Set Modeling for the Risk Analysis for Critical Infrastructure Protection." The Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1250427229.

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Martin, Russell Andrew. "Paths, sampling, and markov chain decomposition." Diss., Georgia Institute of Technology, 2001. http://hdl.handle.net/1853/29383.

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Arendt, Christopher D. "Adaptive Pareto Set Estimation for Stochastic Mixed Variable Design Problems." Ft. Belvoir : Defense Technical Information Center, 2009. http://handle.dtic.mil/100.2/ADA499860.

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Wynn, Troy Alden. "Statistical Analysis of the USU Lidar Data Set with Reference to Mesospheric Solar Response and Cooling Rate Calculation, with Analysis of Statistical Issues Affecting the Regression Coefficients." DigitalCommons@USU, 2010. https://digitalcommons.usu.edu/etd/797.

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Though the least squares technique has many advantages, its possible limitations as applied in the atmospheric sciences have not yet been fully explored in the literature. The assumption that the atmosphere responds either in phase or out of phase to the solar input is ubiquitous. However, our analysis found this assumption to be incorrect. If not properly addressed, the possible consequences are bias in the linear trend coefficient and attenuation of the solar response coefficient. Using USU Rayleigh lidar temperature data, we found a significant phase offset to the solar input in the temperatures that varies ±5 years depending on altitude. In addition to introducing a phase offset into the linear regression model, we argue that separating what we identify as the solar-noise is to be preferred because (1) the solar-noise can contain important physical information, (2) its omission could lead to spurious conclusions about the significance of the solar-proxy coefficient, and (3) its omission could also bias the solar proxy coefficient. We also argue that the Mt. Pinatubo eruption caused a positive temperature perturbation in our early mesopause temperatures, exerting leverage on the linear trend coefficient. In the upper mesosphere, we found a linear cooling trend of greater than -1.5 K/year, which is possibly exaggerated because of leverage from the earlier temperatures and/or collinearity. In the middle mesosphere we found a cooling trend of -1 K/year to near zero. We use the autocorrelation coefficient of the model residuals as a physical parameter. The autocorrelation can provide information about how strongly current temperatures are affected by prior temperatures or how quickly a physical process is occurring. The amplitudes and phases of the annual oscillation in our data compare favorably with those from the OHP and CEL French lidars, as well has the HALOE satellite instrument measurements. The semiannual climatology from the USU temperatures is similar to that from the HALOE temperatures. We also found that our semiannual and annual amplitudes and phases compare favorably with those from the HALOE, OHP, and CPC data.
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Wright, Christopher M. "Using Statistical Methods to Determine Geolocation Via Twitter." TopSCHOLAR®, 2014. http://digitalcommons.wku.edu/theses/1372.

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With the ever expanding usage of social media websites such as Twitter, it is possible to use statistical inquires to form a geographic location of a person using solely the content of their tweets. According to a study done in 2010, Zhiyuan Cheng, was able to detect a location of a Twitter user within 100 miles of their actual location 51% of the time. While this may seem like an already significant find, this study was done while Twitter was still finding its ground to stand on. In 2010, Twitter had 75 million unique users registered, as of March 2013, Twitter has around 500 million unique users. In this thesis, my own dataset was collected and using Excel macros, a comparison of my results to that of Cheng’s will see if the results have changed over the three years since his study. If found to be that Cheng’s 51% can be shown more efficiently using a simpler methodology, this could have a significant impact on Homeland Security and cyber security measures.
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Books on the topic "Statistical set"

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I, Bykov V., ed. Set-summirovanie. Novosibirsk: VO "Nauka", 1993.

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Shvyrkov, V. V. Statistical analysis of data-set quality. 2nd ed. San Francisco: G. Throwkoff Press, 1985.

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Feynman, Richard Phillips. Statistical mechanics: A set of lectures. Boulder, Colo: Westview Press, 1998.

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Canada, Canada Agriculture, ed. Optimal set covering for biological classification. Ottawa: Agriculture Canada, 1993.

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Hughes, William O. Statistical analysis of a large sample size pyroshock test data set. [Cleveland, Ohio]: National Aeronautics and Space Administration, Lewis Research Center, 1997.

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Hughes, William O. Statistical analysis of a large sample size pyroshock test data set. Cleveland, Ohio: National Aeronautics and Space Administration, Lewis Research Center, 1998.

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Hughes, William O. Statistical analysis of a large sample size pyroshock test data set. [Cleveland, Ohio]: National Aeronautics and Space Administration, Lewis Research Center, 1997.

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R, Botet, ed. Aggregation and fractal aggregates. Singapore: World Scientific, 1987.

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NMDS Conference (1992 Edmonton, Alta.). Papers from the Nursing Minimum Data Set Conference: 27-29 October, 1992, Edmonton, Alberta. Ottawa, Ont: Canadian Nurses Association, 1993.

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Health, Washington (State) Dept of. Washington health data set inventory: A collection of resources within the Department of Health for data users. [Olympia, Wash.]: The Dept., 1991.

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Book chapters on the topic "Statistical set"

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Mordeson, John N., Mark J. Wierman, Terry D. Clark, Alex Pham, and Michael A. Redmond. "Set–Valued Statistical Methods." In Linear Models in the Mathematics of Uncertainty, 177–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35224-9_11.

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Wolfe, Douglas A. "Ranked Set Sampling." In International Encyclopedia of Statistical Science, 1185–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04898-2_476.

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Golden, Richard M. "Set Theory for Concept Modeling." In Statistical Machine Learning, 65–81. First edition. j Boca Raton, FL : CRC Press, 2020. j Includes bibliographical references and index.: Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9781351051507-2.

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Segre, Alberto Maria, Andrew Wildenberg, Veronica Vieland, and Ying Zhang. "Privacy-Preserving Data Set Union." In Privacy in Statistical Databases, 266–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11930242_23.

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Nussbaum, Eyal, and Michael Segal. "Privacy Analysis of Query-Set-Size Control." In Privacy in Statistical Databases, 183–94. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-57521-2_13.

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Abujiya, Mu’azu Ramat, and Muhammad Hisyam Lee. "Enhanced Cumulative Sum Charts Based on Ranked Set Sampling." In Statistical Quality Technologies, 79–108. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20709-0_4.

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Gediga, Günther, and Ivo Düntsch. "Statistical Techniques for Rough Set Data Analysis." In Rough Set Methods and Applications, 545–65. Heidelberg: Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1840-6_11.

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Casella, George, J. T. Gene Hwang, and Christian P. Robert. "Loss Functions for Set Estimation." In Statistical Decision Theory and Related Topics V, 237–51. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4612-2618-5_18.

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Wellmer, Friedrich-Wilhelm. "Treatment of the Data Set." In Statistical Evaluations in Exploration for Mineral Deposits, 7–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-60262-7_2.

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Lin, Pei-Sheng, Jun Zhu, Shu-Fu Kuo, and Katherine Curtis. "A Statistical Method for Change-Set Analysis." In Statistical Applications from Clinical Trials and Personalized Medicine to Finance and Business Analytics, 281–91. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42568-9_21.

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Conference papers on the topic "Statistical set"

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Nguyen, Anh-Duc, Seonghwa Choi, Woojae Kim, Sanghoon Lee, and Weisi Lin. "Statistical Convolution On Unordered Point Set." In 2020 IEEE International Conference on Image Processing (ICIP). IEEE, 2020. http://dx.doi.org/10.1109/icip40778.2020.9190709.

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Tsang, Eric C. C., and Su-Yun Zhao. "Random analysis of statistical rough set." In 2016 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2016. http://dx.doi.org/10.1109/icmlc.2016.7860875.

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Tsumoto, Shusaku. "Statistical test for rough set approximation." In AeroSense 2002, edited by Belur V. Dasarathy. SPIE, 2002. http://dx.doi.org/10.1117/12.460255.

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Nazarian, S., M. Pedram, S. K. Gupta, and M. A. Breuer. "STAX: Statistical Crosstalk Target Set Compaction." In 2006 Design, Automation and Test in Europe. IEEE, 2006. http://dx.doi.org/10.1109/date.2006.243844.

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Krishnamurthy, Kalyani, Waheed U. Bajwa, Rebecca Willett, and Robert Calderbank. "Fast level set estimation from projection measurements." In 2011 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2011. http://dx.doi.org/10.1109/ssp.2011.5967766.

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Xu, Hao, Alexander Lorbert, Peter J. Ramadge, J. Swaroop Guntupalli, and James V. Haxby. "Regularized hyperalignment of multi-set fMRI data." In 2012 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2012. http://dx.doi.org/10.1109/ssp.2012.6319668.

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Ganikhodjaev, Nasir, and Nur Zatul Akmar Hamzah. "Geometric quadratic stochastic operator on countable infinite set." In THE 2ND ISM INTERNATIONAL STATISTICAL CONFERENCE 2014 (ISM-II): Empowering the Applications of Statistical and Mathematical Sciences. AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4907516.

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Tsumoto, Shusaku. "Statistical extension of rough set rule induction." In Aerospace/Defense Sensing, Simulation, and Controls, edited by Belur V. Dasarathy. SPIE, 2001. http://dx.doi.org/10.1117/12.421072.

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Savas, Ekrem. "On asymptotically generalized statistical equivalent set sequences." In 11TH INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS 2013: ICNAAM 2013. AIP, 2013. http://dx.doi.org/10.1063/1.4825606.

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Meson, A. M., and F. Vericat. "A formula on the pressure for a set of generic points." In NONEQUILIBRIUM STATISTICAL PHYSICS TODAY: Proceedings of the 11th Granada Seminar on Computational and Statistical Physics. AIP, 2011. http://dx.doi.org/10.1063/1.3569547.

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Reports on the topic "Statistical set"

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Detcheva, Albena K., and Vasil D. Simeonov. Multivariate Statistical Interpretation of a Data Set of Medieval Glass Fragments. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, January 2010. http://dx.doi.org/10.7546/crabs.2020.01.05.

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Grigsby, Claude C., Ryan M. Kramer, Michael A. Zmuda, Derek W. Boone, Tyler C. Highlander, and Mateen M. Rizki. Differential Profiling of Volatile Organic Compound Biomarker Signatures Utilizing a Logical Statistical Filter-Set and Novel Hybrid Evolutionary Classifiers. Fort Belvoir, VA: Defense Technical Information Center, April 2012. http://dx.doi.org/10.21236/ada562341.

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LeGrand, Keith, Raymond H. Byrne, Pavan Datta, David K. Melgaard, and Johnathan Mulcahy-Stanislawczyk. Finite Set Statistics Based Multitarget Tracking. Office of Scientific and Technical Information (OSTI), September 2017. http://dx.doi.org/10.2172/1397134.

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Zakharov, Vladimir, and Andrei Pushkarev. Deterministic and Advanced Statistical Modeling of Wind-Driven Sea. Fort Belvoir, VA: Defense Technical Information Center, July 2015. http://dx.doi.org/10.21236/ada622079.

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Maidanik, G., and K. J. Becker. Are the Energy Analysis (EA) and the Statistical Energy Analysis (SEA) Compatible? Fort Belvoir, VA: Defense Technical Information Center, November 2003. http://dx.doi.org/10.21236/ada419012.

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Gilbert, R. O. A review of statistical methods for data sets with multiple censoring points. Office of Scientific and Technical Information (OSTI), July 1995. http://dx.doi.org/10.2172/650341.

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Sandstrom, Mary M., Geoffrey W. Brown, Daniel N. Preston, Colin J. Pollard, Kirstin F. Warner, Daniel N. Sorensen, Daniel L. Remmers, et al. Integrated Data Collection Analysis (IDCA) Program - Statistical Analysis of RDX Standard Data Sets. Office of Scientific and Technical Information (OSTI), October 2015. http://dx.doi.org/10.2172/1258546.

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Shaw, Ping-Tung. A Statistical Investigation of Internal Wave Propagation in the Northern South China Sea. Fort Belvoir, VA: Defense Technical Information Center, September 2010. http://dx.doi.org/10.21236/ada542577.

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Shaw, Ping-Tung. A Statistical Investigation of Internal Wave Propagation in the Northern South China Sea. Fort Belvoir, VA: Defense Technical Information Center, September 2012. http://dx.doi.org/10.21236/ada590339.

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Shaw, Ping-Tung. A Statistical Investigation of Internal Wave Propagation in the Northern South China Sea. Fort Belvoir, VA: Defense Technical Information Center, September 2011. http://dx.doi.org/10.21236/ada557078.

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