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Journal articles on the topic 'Statistical set'

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Published: 4 June 2021
Last updated: 1 February 2022

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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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Hosoya, Yuzo. "Inference on a Set of Statistical Models." JOURNAL OF THE JAPAN STATISTICAL SOCIETY 38, no. 1 (2008): 107–18. http://dx.doi.org/10.14490/jjss.38.107.

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12

DÜNTSCH, IVO, and GÜNTHER GEDIGA. "Statistical evaluation of rough set dependency analysis." International Journal of Human-Computer Studies 46, no. 5 (May 1997): 589–604. http://dx.doi.org/10.1006/ijhc.1996.0105.

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13

Cremers, Daniel. "Statistical shape priors for level set segmentation." PAMM 7, no. 1 (December 2007): 1041903–4. http://dx.doi.org/10.1002/pamm.200700148.

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14

de Leeuw, Christiaan A., Benjamin M. Neale, Tom Heskes, and Danielle Posthuma. "The statistical properties of gene-set analysis." Nature Reviews Genetics 17, no. 6 (April 12, 2016): 353–64. http://dx.doi.org/10.1038/nrg.2016.29.

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15

Combettes, P. L., and T. J. Chaussalet. "Combining statistical information in set theoretic estimation." IEEE Signal Processing Letters 3, no. 3 (March 1996): 61–62. http://dx.doi.org/10.1109/97.481155.

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16

Ghosal, Sanjoy, and Avishek Ghosh. "When deviation happens between rough statistical convergence and rough weighted statistical convergence." Mathematica Slovaca 69, no. 4 (August 27, 2019): 871–90. http://dx.doi.org/10.1515/ms-2017-0275.

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Abstract In this paper we introduce rough weighted statistical limit set and weighted statistical cluster points set which are natural generalizations of rough statistical limit set and statistical cluster points set of double sequences respectively. Some new examples are constructed to ensure the deviation of basic results. Both the sets don’t follow the usual extension properties which will be discussed here.
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17

Takayanagi, Sumiko, and Norman Cliff. "An Examination of Graduate Students' Statistical Judgments: Statistical and Fuzzy Set Approaches." Psychological Reports 86, no. 1 (February 2000): 243–59. http://dx.doi.org/10.2466/pr0.2000.86.1.243.

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The present study examined how statistical significance levels are treated and interpreted by graduate students who use hypothesis-testing in their scientific investigation. To test underlying psychological aspects of hypothesis-testing, the idea of fuzzy set theory was employed to identify the uncertain points in judgments. 34 graduate students in a psychology department made judgments about hypothetical statistical decisions. The results indicated that (1) the majority of these students treated significance levels on a continuum and rated them according to the magnitude of statistical significance; (2) the subjects shifted their decisions based on the types of hypothetical scenarios but not by the sample sizes; instead, they interpreted a smaller sample size as being less reliable. (3) The subjects frequently chose formally used statistical terms, e.g., Significant and Not Significant, more than graduated verbal expressions, e.g., Marginally Significant and Borderline Significant; and (4) the Fuzziness (degree of confidence in decision-making) was dependent on individuals and existed more in the critical points of transition where judgments are most difficult. The Fuzziness Index illustrated the subtle shifts of human decision-making patterns in statistical judgments. Underlying decision uncertainties and difficulties can be illustrated by functions generated from fuzzy set theory, which may more closely resemble human psychological mechanism. This integrative study of fuzzy set theory and behavioral measurements appears to provide a technique that is more natural for examining and understanding imprecise boundaries of human decisions.
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18

Taylor, Jonathan, and Robert J. Tibshirani. "Statistical learning and selective inference." Proceedings of the National Academy of Sciences 112, no. 25 (June 23, 2015): 7629–34. http://dx.doi.org/10.1073/pnas.1507583112.

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We describe the problem of “selective inference.” This addresses the following challenge: Having mined a set of data to find potential associations, how do we properly assess the strength of these associations? The fact that we have “cherry-picked”—searched for the strongest associations—means that we must set a higher bar for declaring significant the associations that we see. This challenge becomes more important in the era of big data and complex statistical modeling. The cherry tree (dataset) can be very large and the tools for cherry picking (statistical learning methods) are now very sophisticated. We describe some recent new developments in selective inference and illustrate their use in forward stepwise regression, the lasso, and principal components analysis.
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19

Marchant, A., and J. de Fockert. "Effects of set size and heterogeneity in set representation by statistical properties." Journal of Vision 10, no. 7 (August 17, 2010): 1262. http://dx.doi.org/10.1167/10.7.1262.

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20

Sorooshian, Shahryar. "Fuzzy Approach to Statistical Control Charts." Journal of Applied Mathematics 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/745153.

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After investigating the advantages and disadvantages of current methods of statistical process control, it becomes important to overcome the disadvantages and then use the advantages to improve a method for monitoring a process with categorical observations. An approach which considers uncertainty and vagueness is tried for this study; and for this purpose, fuzzy set theory is inevitable to use. So, a new approach based on fuzzy set theory is introduced in this research for monitoring attribute quality characteristics. This approach is then compared with the current related approach to see the difference in performance.
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21

Seo, Sun Won, Young Chae Cho, Woo Sung Park, and Kwang Hwan Kim. "Statistical Analysis of Diseases with Discharge Data Set." Journal of Korean Society of Medical Informatics 6, no. 1 (2000): 23. http://dx.doi.org/10.4258/jksmi.2000.6.1.23.

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22

Yang, Jianxin, Junying Wang, Zhaoqiang Wu, and Nabil Anwer. "Statistical Tolerancing based on Variation of Point-set." Procedia CIRP 10 (2013): 9–16. http://dx.doi.org/10.1016/j.procir.2013.08.006.

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23

Glaser, Allan. "A Set of Quality Criteria for Statistical Programming." Drug Information Journal 36, no. 3 (July 2002): 565–70. http://dx.doi.org/10.1177/009286150203600311.

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24

Munneke, Jaap, and Jennifer Corbett. "Interactions between statistical set representations and visual stability." Journal of Vision 18, no. 10 (September 1, 2018): 83. http://dx.doi.org/10.1167/18.10.83.

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25

Everett, Jess W., Timothy L. Jacobs, and J. Jeffrey Peirce. "Recycling Promotion Strategies: Statistical and Fuzzy‐Set Comparisons." Journal of Urban Planning and Development 117, no. 4 (December 1991): 154–67. http://dx.doi.org/10.1061/(asce)0733-9488(1991)117:4(154).

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26

Choi, YounJeong, and Christina Kendziorski. "Statistical methods for gene set co-expression analysis." Bioinformatics 25, no. 21 (August 18, 2009): 2780–86. http://dx.doi.org/10.1093/bioinformatics/btp502.

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27

Efstathiou, George, and Pablo Lemos. "Statistical inconsistencies in the KiDS-450 data set." Monthly Notices of the Royal Astronomical Society 476, no. 1 (January 12, 2018): 151–57. http://dx.doi.org/10.1093/mnras/sty099.

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28

Cosman, Joshua D., and Shaun P. Vecera. "Establishment of an attentional set via statistical learning." Journal of Experimental Psychology: Human Perception and Performance 40, no. 1 (February 2014): 1–6. http://dx.doi.org/10.1037/a0034489.

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29

Frey, Jesse. "Distribution-free statistical intervals via ranked-set sampling." Canadian Journal of Statistics 35, no. 4 (December 2007): 585–96. http://dx.doi.org/10.1002/cjs.5550350409.

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30

Franke, Björn. "Statistical Performance Modeling in Functional Instruction Set Simulators." ACM Transactions on Embedded Computing Systems 11S, no. 1 (June 2012): 1–22. http://dx.doi.org/10.1145/2180887.2180899.

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31

Muttlak, Hassen, and Walid Al-Sabah. "Statistical quality control based on ranked set sampling." Journal of Applied Statistics 30, no. 9 (November 2003): 1055–78. http://dx.doi.org/10.1080/0266476032000076173.

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32

Zhao, Jin-Hua, Yusupjan Habibulla, and Hai-Jun Zhou. "Statistical Mechanics of the Minimum Dominating Set Problem." Journal of Statistical Physics 159, no. 5 (February 21, 2015): 1154–74. http://dx.doi.org/10.1007/s10955-015-1220-2.

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33

Ong, Meng Sang, Ye Chow Kuang, and Melanie Po-Leen Ooi. "Statistical measures of two dimensional point set uniformity." Computational Statistics & Data Analysis 56, no. 6 (June 2012): 2159–81. http://dx.doi.org/10.1016/j.csda.2011.12.005.

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34

Penttinen, Antti, and Aki Niemi. "On Statistical Inference for the Random Set Generated Cox Process with Set-marking." Biometrical Journal 49, no. 2 (April 2007): 197–213. http://dx.doi.org/10.1002/bimj.200610272.

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35

MOCZURAD, M., J. TYSZKIEWICZ, and M. ZAIONC. "Statistical properties of simple types." Mathematical Structures in Computer Science 10, no. 5 (October 2000): 575–94. http://dx.doi.org/10.1017/s0960129599002959.

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We consider types and typed lambda calculus over a finite number of ground types. We are going to investigate the size of the fraction of inhabited types of the given length n against the number of all types of length n. The plan of this paper is to find the limit of that fraction when n → ∞. The answer to this question is equivalent to finding the ‘density’ of inhabited types in the set of all types, or the so-called asymptotic probability of finding an inhabited type in the set of all types. Under the Curry–Howard isomorphism this means finding the density or asymptotic probability of provable intuitionistic propositional formulas in the set of all formulas. For types with one ground type (formulas with one propositional variable), we prove that the limit exists and is equal to 1/2 + √5/10, which is approximately 72.36%. This means that a long random type (formula) has this probability of being inhabited (tautology). We also prove that for every finite number k of ground-type variables, the density of inhabited types is always positive and lies between (4k + 1)/(2k + 1)2 and (3k + 1)/(k + 1)2. Therefore we can easily see that the density is decreasing to 0 with k going to infinity. From the lower and upper bounds presented we can deduce that at least 1/3 of classical tautologies are intuitionistic.
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36

Eygü, Hakan, and M. Suphi Özçomak. "Multivariate Statistical Quality Control Based on Ranked Set Sampling." Asian Social Science 14, no. 1 (December 14, 2017): 1. http://dx.doi.org/10.5539/ass.v14n1p1.

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The sample of the study was formed using simple random sampling, ranked set sampling, extreme ranked set sampling and median ranked set sampling. At the end of this process, the researcher created Hotelling’s T2 control charts, a multivariate statistical process control method. The performances of SRS, RSS, ERSS and MRSS sampling methods were compared to one another using these control charts. A simulation was performed to see the average run-length values for Hotelling’s T2 control charts, and these findings were also used for the comparison of the sampling performances.At the end of the study, the researcher formed a sample using median ranked set sampling and created the Hotelling’s T2 control chart. As a result of this operation, the researcher found that there was an out-of-control signal in the process, while there was no such signal in other sampling methods. When the average run-length values obtained from Hotelling’s T2 control charts were compared, it was seen that a shift in the process was detected by the ranked set sampling earlier, when compared to other sampling methods. This paper it can be said that the methods used are unique to the literature because they are applied to multivariate data.
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37

LUOR, DAH-CHIN. "STATISTICAL PROPERTIES OF LINEAR FRACTAL INTERPOLATION FUNCTIONS FOR RANDOM DATA SETS." Fractals 26, no. 01 (February 2018): 1850009. http://dx.doi.org/10.1142/s0218348x18500093.

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Let [Formula: see text] be an integer greater than or equal to [Formula: see text] and let [Formula: see text] be numbers with [Formula: see text]. Denote that [Formula: see text] is the interval [Formula: see text] and [Formula: see text] is a set of points. Suppose that [Formula: see text] is a random perturbation of [Formula: see text] for [Formula: see text], and we set [Formula: see text]. Let [Formula: see text] and [Formula: see text] be linear fractal interpolation functions on [Formula: see text] corresponding to the set of points [Formula: see text] and [Formula: see text], respectively. The value [Formula: see text] is random for all [Formula: see text]. In this paper, we show that the expectation of [Formula: see text] is [Formula: see text]. We also establish estimations for the variance of [Formula: see text] and the expectation of [Formula: see text].
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38

Zeager, Jeff. "Statistical limit point theorems." International Journal of Mathematics and Mathematical Sciences 23, no. 11 (2000): 741–52. http://dx.doi.org/10.1155/s0161171200002088.

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It is known that given a regular matrixAand a bounded sequencexthere is a subsequence (respectively, rearrangement, stretching)yofxsuch that the set of limit points ofAyincludes the set of limit points ofx. Using the notion of a statistical limit point, we establish statistical convergence analogues to these results by proving that every complex number sequencexhas a subsequence (respectively, rearrangement, stretching)ysuch that every limit point ofxis a statistical limit point ofy. We then extend our results to the more generalA-statistical convergence, in whichAis an arbitrary nonnegative matrix.
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39

SENGUL, HACER, and MIKAIL ET. "Lacunary statistical convergence of order (α, β) in topological groups." Creative Mathematics and Informatics 26, no. 3 (2017): 339–44. http://dx.doi.org/10.37193/cmi.2017.03.11.

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In this paper, the concept of lacunary statistical convergence of order (α, β) is generalized to topological groups, and some inclusion relations between the set of all statistically convergent sequences of order (α, β) and the set of all lacunary statistically convergent sequences of order (α, β) are given.
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40

Selmer, Sarah J., Johnna J. Bolyard, and James A. Rye. "Statistical Reasoning over Lunch." Mathematics Teaching in the Middle School 17, no. 5 (December 2011): 274–81. http://dx.doi.org/10.5951/mathteacmiddscho.17.5.0274.

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41

BURGIN, MARK, and OKTAY DUMAN. "STATISTICAL FUZZY CONVERGENCE." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 16, no. 06 (December 2008): 879–902. http://dx.doi.org/10.1142/s0218488508005674.

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The goal of this work is the further development of neoclassical analysis, which extends the scope and results of the classical mathematical analysis by applying fuzzy logic to conventional mathematical objects, such as functions, sequences, and series. This allows us to reflect and model vagueness and uncertainty of our knowledge, which results from imprecision of measurement and inaccuracy of computation. Basing on the theory of fuzzy limits, we develop the structure of statistical fuzzy convergence and study its properties. Relations between statistical fuzzy convergence and fuzzy convergence are considered in the First Subsequence Theorem and the First Reduction Theorem. Algebraic structures of statistical fuzzy limits are described in the Linearity Theorem. Topological structures of statistical fuzzy limits are described in the Limit Set Theorem and Limit Fuzzy Set theorems. Relations between statistical convergence, statistical fuzzy convergence, ergodic systems, fuzzy convergence and convergence of statistical characteristics, such as the mean (average), and standard deviation, are studied in Secs. 2 and 4. Introduced constructions and obtained results open new directions for further research that are considered in the Conclusion.
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42

Beal, Kathleen G., and Harry J. Khamis. "Statistical Analysis of a Problem Data Set: Correlated Observations." Condor 92, no. 1 (February 1990): 248–51. http://dx.doi.org/10.2307/1368411.

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43

Bremner, Frederick J., Michael Yost, and Victoria T. Nasman. "Statistical analysis of fuzzy-set data from neuronal networks." Behavior Research Methods, Instruments, & Computers 21, no. 2 (March 1989): 209–12. http://dx.doi.org/10.3758/bf03205584.

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44

Hai-Song, XU, LI Xiao-Qin, and ZENG Yi. "Statistical Coupling Analysis of a SH3 Domain Sequence Set." Acta Physico-Chimica Sinica 27, no. 10 (2011): 2447–56. http://dx.doi.org/10.3866/pku.whxb20111009.

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45

Xia, Xintao, Zhongyu Wang, and Yongsheng Gao. "Estimation of non-statistical uncertainty using fuzzy-set theory." Measurement Science and Technology 11, no. 4 (March 10, 2000): 430–35. http://dx.doi.org/10.1088/0957-0233/11/4/314.

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46

Cremers, D. "Dynamical statistical shape priors for level set-based tracking." IEEE Transactions on Pattern Analysis and Machine Intelligence 28, no. 8 (August 2006): 1262–73. http://dx.doi.org/10.1109/tpami.2006.161.

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47

Maciejewski, H. "Gene set analysis methods: statistical models and methodological differences." Briefings in Bioinformatics 15, no. 4 (February 14, 2013): 504–18. http://dx.doi.org/10.1093/bib/bbt002.

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48

Potter, Randall W., and George W. Sturm. "Detecting test set hardware degradation using statistical data modelling." Quality and Reliability Engineering International 9, no. 1 (January 1993): 63–67. http://dx.doi.org/10.1002/qre.4680090111.

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49

Tillmann, Christoph, and Tong Zhang. "An Online Relevant Set Algorithm for Statistical Machine Translation." IEEE Transactions on Audio, Speech, and Language Processing 16, no. 7 (September 2008): 1274–86. http://dx.doi.org/10.1109/tasl.2008.921760.

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50

Rogerson, Peter A. "A set of associated statistical tests for spatial clustering." Environmental and Ecological Statistics 12, no. 3 (September 2005): 275–88. http://dx.doi.org/10.1007/s10651-005-1513-8.

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