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Journal articles on the topic 'Random selection'

To see the other types of publications on this topic, follow the link: Random selection.

Author: Grafiati

Published: 4 June 2021

Last updated: 25 April 2022

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1

Cortines, Aser, and Bastien Mallein. "A N-branching random walk with random selection." Latin American Journal of Probability and Mathematical Statistics 14, no. 1 (2017): 117. http://dx.doi.org/10.30757/alea.v14-07.

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2

ARLOTTO, ALESSANDRO, and J. MICHAEL STEELE. "Optimal Sequential Selection of a Unimodal Subsequence of a Random Sequence." Combinatorics, Probability and Computing 20, no. 6 (October 5, 2011): 799–814. http://dx.doi.org/10.1017/s0963548311000411.

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We consider the problem of selecting sequentially a unimodal subsequence from a sequence of independent identically distributed random variables, and we find that a person doing optimal sequential selection does so within a factor of the square root of two as well as a prophet who knows all of the random observations in advance of any selections. Our analysis applies in fact to selections of subsequences that have d+1 monotone blocks, and, by including the case d=0, our analysis also covers monotone subsequences.

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3

Stone, Peter. "A Renaissance for Random Selection?" Redescriptions: Political Thought, Conceptual History and Feminist Theory 16, no. 1 (January 1, 2013): 148. http://dx.doi.org/10.7227/r.16.1.8.

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4

Genuer, Robin, Jean-Michel Poggi, and Christine Tuleau-Malot. "Variable selection using random forests." Pattern Recognition Letters 31, no. 14 (October 2010): 2225–36. http://dx.doi.org/10.1016/j.patrec.2010.03.014.

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5

Morse, Janice M. "What's Wrong with Random Selection?" Qualitative Health Research 8, no. 6 (November 1998): 733–35. http://dx.doi.org/10.1177/104973239800800601.

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6

Bissell, A. F. "Ordered Random Selection Without Replacement." Applied Statistics 35, no. 1 (1986): 73. http://dx.doi.org/10.2307/2347867.

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7

Boland, Philip J., and Kevin Hutchinson. "Student Selection of Random Digits." Journal of the Royal Statistical Society: Series D (The Statistician) 49, no. 4 (December 2000): 519–29. http://dx.doi.org/10.1111/1467-9884.00250.

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8

Stone, Peter. "The Logic of Random Selection." Political Theory 37, no. 3 (February 11, 2009): 375–97. http://dx.doi.org/10.1177/0090591709332329.

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9

Arlotto, Alessandro, Robert W. Chen, Lawrence A. Shepp, and J. Michael Steele. "Online Selection of Alternating Subsequences from a Random Sample." Journal of Applied Probability 48, no. 04 (December 2011): 1114–32. http://dx.doi.org/10.1017/s0021900200008652.

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We consider sequential selection of an alternating subsequence from a sequence of independent, identically distributed, continuous random variables, and we determine the exact asymptotic behavior of an optimal sequentially selected subsequence. Moreover, we find (in a sense we make precise) that a person who is constrained to make sequential selections does only about 12 percent worse than a person who can make selections with full knowledge of the random sequence.

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10

Arlotto, Alessandro, Robert W. Chen, Lawrence A. Shepp, and J. Michael Steele. "Online Selection of Alternating Subsequences from a Random Sample." Journal of Applied Probability 48, no. 4 (December 2011): 1114–32. http://dx.doi.org/10.1239/jap/1324046022.

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We consider sequential selection of an alternating subsequence from a sequence of independent, identically distributed, continuous random variables, and we determine the exact asymptotic behavior of an optimal sequentially selected subsequence. Moreover, we find (in a sense we make precise) that a person who is constrained to make sequential selections does only about 12 percent worse than a person who can make selections with full knowledge of the random sequence.

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11

Jameel, Furqan, Faisal Khan, M. Asif Ali Haider, and Amir Aziz Butt. "Secure Path Selection under Random Fading." Advances in Science, Technology and Engineering Systems Journal 2, no. 3 (May 2017): 376–83. http://dx.doi.org/10.25046/aj020348.

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12

Dhyaram, Lakshmi Padmaja. "RANDOM SUBSET FEATURE SELECTION FOR CLASSIFICATION." International Journal of Advanced Research in Computer Science 9, no. 2 (April 20, 2018): 317–19. http://dx.doi.org/10.26483/ijarcs.v9i2.5496.

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13

Nowak, A. "Random differential inclusions: Measurable selection approach." Annales Polonici Mathematici 49, no. 3 (1989): 291–96. http://dx.doi.org/10.4064/ap-49-3-291-296.

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14

Gwiazda, Jeremy. "God’s Random Selection: Reply to Steinberg." Sophia 49, no. 1 (February 23, 2010): 141–43. http://dx.doi.org/10.1007/s11841-010-0161-0.

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15

Adachi, F., and J. D. Parsons. "Random FM noise with selection combining." IEEE Transactions on Communications 36, no. 6 (June 1988): 752–50. http://dx.doi.org/10.1109/26.2796.

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16

Masulli, Francesco, and Stefano Rovetta. "Random Voronoi ensembles for gene selection." Neurocomputing 55, no. 3-4 (October 2003): 721–26. http://dx.doi.org/10.1016/s0925-2312(03)00377-1.

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17

Lavielle, Marc, and Carenne Ludeña. "Random thresholds for linear model selection." ESAIM: Probability and Statistics 12 (2008): 173–95. http://dx.doi.org/10.1051/ps:2007047.

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18

Wang, Qi, Jia Wan, Feiping Nie, Bo Liu, Chenggang Yan, and Xuelong Li. "Hierarchical Feature Selection for Random Projection." IEEE Transactions on Neural Networks and Learning Systems 30, no. 5 (May 2019): 1581–86. http://dx.doi.org/10.1109/tnnls.2018.2868836.

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19

Bauer, Johannes J. "Selection Errors of Random Route Samples." Sociological Methods & Research 43, no. 3 (February 25, 2014): 519–44. http://dx.doi.org/10.1177/0049124114521150.

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20

GREENBERG, E. R. "RANDOM DIGIT DIALING FOR CONTROL SELECTION." American Journal of Epidemiology 131, no. 1 (January 1990): 1–5. http://dx.doi.org/10.1093/oxfordjournals.aje.a115462.

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21

Mata, Fernando. "Common random numbers and multinomial selection." Computers & Industrial Engineering 25, no. 1-4 (September 1993): 33–36. http://dx.doi.org/10.1016/0360-8352(93)90214-i.

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22

Krishnan, Pramila. "Random parameters and self-selection models." Empirical Economics 18, no. 2 (June 1993): 197–213. http://dx.doi.org/10.1007/bf01205398.

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23

NASS, H. G. "SELECTION FOR GRAIN YIELD OF SPRING WHEAT UTILIZING SEED SIZE AND OTHER SELECTION CRITERIA." Canadian Journal of Plant Science 67, no. 3 (July 1, 1987): 605–10. http://dx.doi.org/10.4141/cjps87-086.

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Four selection procedures, to include random selection, visual head selection by two selectors, selection for large seed size, and harvest index, respectively, were evaluated for relative effectiveness in selecting high-yielding plants in F2 and F3 populations of three crosses of spring wheat (Triticum aestivum L.). Twenty-five F4-derived lines of the three crosses for each selection method were evaluated for yield performance during 2 yr of study. Selection for large seed size and visual head selection produced more late-heading, tall or high-yielding plants than the other selection methods. A larger number of F4-derived lines in the top yielding 5% and 25% within each cross was chosen by visual selection and by selection for large seed size.Key words: Selection methods, seed size, harvest index, grain yield, spring wheat, visual head selections

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24

Abbas, et al. "Tournament selection mechanism based random vector selection in differential evolution algorithm." International Journal of ADVANCED AND APPLIED SCIENCES 4, no. 7 (July 2017): 147–58. http://dx.doi.org/10.21833/ijaas.2017.07.022.

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25

Keller, Merlin, and Marc Lavielle. "Random threshold for linear model selection, revisited." Statistics and Its Interface 5, no. 2 (2012): 263–75. http://dx.doi.org/10.4310/sii.2012.v5.n2.a10.

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26

Deng, Houtao, and George Runger. "Gene selection with guided regularized random forest." Pattern Recognition 46, no. 12 (December 2013): 3483–89. http://dx.doi.org/10.1016/j.patcog.2013.05.018.

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27

Babayan, P. V. "Object selection under random geometric image transformations." Optoelectronics, Instrumentation and Data Processing 46, no. 3 (June 2010): 237–42. http://dx.doi.org/10.3103/s8756699010030052.

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28

Huang, Xiaoxia. "Optimal project selection with random fuzzy parameters." International Journal of Production Economics 106, no. 2 (April 2007): 513–22. http://dx.doi.org/10.1016/j.ijpe.2006.06.011.

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29

Lai, Carmen, Marcel J. T. Reinders, and Lodewyk Wessels. "Random subspace method for multivariate feature selection." Pattern Recognition Letters 27, no. 10 (July 2006): 1067–76. http://dx.doi.org/10.1016/j.patrec.2005.12.018.

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30

Chen, Zhen, and David B. Dunson. "Random Effects Selection in Linear Mixed Models." Biometrics 59, no. 4 (December 2003): 762–69. http://dx.doi.org/10.1111/j.0006-341x.2003.00089.x.

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31

TANG, L., P. ZHU, X. YOU, and Y. WANG. "Multiple Random Beams Selection Exploiting Chordal Distances." IEICE Transactions on Communications E91-B, no. 11 (November 1, 2008): 3722–26. http://dx.doi.org/10.1093/ietcom/e91-b.11.3722.

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32

Peres, Yuval, Oded Schramm, Scott Sheffield, and David B. Wilson. "Random-Turn Hex and Other Selection Games." American Mathematical Monthly 114, no. 5 (May 2007): 373–87. http://dx.doi.org/10.1080/00029890.2007.11920428.

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33

Rudgers, Gary W., and Timothy Palzkill. "Protein minimization by random fragmentation and selection." Protein Engineering, Design and Selection 14, no. 7 (July 2001): 487–92. http://dx.doi.org/10.1093/protein/14.7.487.

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34

Elkhalil, Khalil, Abla Kammoun, Tareq Y. Al-Naffouri, and Mohamed-Slim Alouini. "Measurement Selection: A Random Matrix Theory Approach." IEEE Transactions on Wireless Communications 17, no. 7 (July 2018): 4899–911. http://dx.doi.org/10.1109/twc.2018.2833464.

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35

Chevin, Luis-Miguel. "Species selection and random drift in macroevolution." Evolution 70, no. 3 (March 2016): 513–25. http://dx.doi.org/10.1111/evo.12879.

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36

Glance, B., and O. Scaramucci. "Optical heterodyne receiver providing random channel selection." Electronics Letters 25, no. 19 (1989): 1280. http://dx.doi.org/10.1049/el:19890857.

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37

Shi, Yimin, Yong Xu, and Huiguang Kang. "A Strong Limit Theorem on Random Selection." Southeast Asian Bulletin of Mathematics 25, no. 3 (February 2002): 515–21. http://dx.doi.org/10.1007/s10012-001-0515-1.

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38

Lai, Yuqing, and Yongfeng Men. "Polymorph selection during crystallization of random copolymers." European Polymer Journal 101 (April 2018): 218–24. http://dx.doi.org/10.1016/j.eurpolymj.2018.02.038.

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39

Burkot, T. R. "Non-random host selection by anopheline mosquitoes." Parasitology Today 4, no. 6 (June 1988): 156–62. http://dx.doi.org/10.1016/0169-4758(88)90151-2.

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40

Yoon, Sung-jun, Sang Won Choi, Ki-bum Kwon, Dong-hyun Park, and Jianjun Li. "Enhanced Random Resource Selection Scheme for V2X." Journal of Korean Institute of Communications and Information Sciences 42, no. 5 (May 31, 2017): 1058–68. http://dx.doi.org/10.7840/kics.2017.42.5.1058.

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41

Dickerson, Pamela S. "Picking a Planner: More than Random Selection." Journal of Continuing Education in Nursing 41, no. 6 (June 1, 2010): 242–43. http://dx.doi.org/10.3928/00220124-20100525-04.

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42

Babu, B. Sathish, and Pallapa Venkataram. "Random security scheme selection for mobile transactions." Security and Communication Networks 2, no. 6 (May 5, 2009): 694–708. http://dx.doi.org/10.1002/sec.113.

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43

Ishwaran, Hemant, Thomas A. Gerds, Udaya B. Kogalur, Richard D. Moore, Stephen J. Gange, and Bryan M. Lau. "Random survival forests for competing risks." Biostatistics 15, no. 4 (April 11, 2014): 757–73. http://dx.doi.org/10.1093/biostatistics/kxu010.

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Abstract We introduce a new approach to competing risks using random forests. Our method is fully non-parametric and can be used for selecting event-specific variables and for estimating the cumulative incidence function. We show that the method is highly effective for both prediction and variable selection in high-dimensional problems and in settings such as HIV/AIDS that involve many competing risks.

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44

Rowe, D. E. "Effects of random and non-random errors on phenotypic selection in autotetraploids." Theoretical and Applied Genetics 69, no. 3 (1985): 317–23. http://dx.doi.org/10.1007/bf00662452.

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45

Malekipirbazari, Milad, Vural Aksakalli, Waleed Shafqat, and Andrew Eberhard. "Performance comparison of feature selection and extraction methods with random instance selection." Expert Systems with Applications 179 (October 2021): 115072. http://dx.doi.org/10.1016/j.eswa.2021.115072.

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46

Neuhauser, Claudia, and Stephen M. Krone. "The Genealogy of Samples in Models With Selection." Genetics 145, no. 2 (February 1, 1997): 519–34. http://dx.doi.org/10.1093/genetics/145.2.519.

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We introduce the genealogy of a random sample of genes taken from a large haploid population that evolves according to random reproduction with selection and mutation. Without selection, the genealogy is described by Kingman's well-known coalescent process. In the selective case, the genealogy of the sample is embedded in a graph with a coalescing and branching structure. We describe this graph, called the ancestral selection graph, and point out differences and similarities with Kingman's coalescent. We present simulations for a two-allele model with symmetric mutation in which one of the alleles has a selective advantage over the other. We find that when the allele frequencies in the population are already in equilibrium, then the genealogy does not differ much from the neutral case. This is supported by rigorous results. Furthermore, we describe the ancestral selection graph for other selective models with finitely many selection classes, such as the K-allele models, infinitely-many-alleles models, DNA sequence models, and infinitely-many-sites models, and briefly discuss the diploid case.

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47

Adkison, Milo D. "Population differentiation in Pacific salmons: local adaptation genetic drift, or the environment?" Canadian Journal of Fisheries and Aquatic Sciences 52, no. 12 (December 1, 1995): 2762–77. http://dx.doi.org/10.1139/f95-865.

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Morphological, behavioral, and life-history differences between Pacific salmon (Oncorhynchus spp.) populations are commonly thought to reflect local adaptation, and it is likewise common to assume that salmon populations separated by small distances are locally adapted. Two alternatives to local adaptation exist: random genetic differentiation owing to genetic drift and founder events, and genetic homogeneity among populations, in which differences reflect differential trait expression in differing environments. Population genetics theory and simulations suggest that both alternatives are possible. With selectively neutral alleles, genetic drift can result in random differentiation despite many strays per generation. Even weak selection can prevent genetic drift in stable populations; however, founder effects can result in random differentiation despite selective pressures. Overlapping generations reduce the potential for random differentiation. Genetic homogeneity can occur despite differences in selective regimes when straying rates are high. In sum, localized differences in selection should not always result in local adaptation. Local adaptation is favored when population sizes are large and stable, selection is consistent over large areas, selective differentials are large, and straying rates are neither too high nor too low. Consideration of alternatives to adaptation would improve both biological research and salmon conservation efforts.

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48

Birgé, Lucien. "Model selection for Gaussian regression with random design." Bernoulli 10, no. 6 (December 2004): 1039–51. http://dx.doi.org/10.3150/bj/1106314849.

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49

Marken, Richard. "Selection of Consequences: Adaptive Behavior from Random Reinforcement." Psychological Reports 56, no. 2 (April 1985): 379–83. http://dx.doi.org/10.2466/pr0.1985.56.2.379.

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The behavior of subjects in a human operant conditioning experiment was “shaped” using a random reinforcement contingency. Bar-press responses kept a moving cursor near a target although the consequence of each response was a random change in the direction of the cursor. The apparent effect of reinforcement on behavior is shown to be an illusion created by ignoring the consistency of behavioral results.

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50

Zhu, Min, Jing Xia, Mo Lei Yan, Sheng Yu Zhang, Guo Long Cai, Jing Yan, and Gang Min Ning. "Feature Selection and Optimization of Random Forest Modeling." Applied Mechanics and Materials 687-691 (November 2014): 1416–19. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.1416.

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Traditional random forest algorithm is difficult to achieve very good effect for the classification of small sample data set. Because in the process of repeated random selection, selection sample is little, resulting in trees with very small degree of difference, which floods right decisions, makes bigger generalization error of the model, and the predict rate is reduced. For the sample size of sepsis cases data, this paper adopts for parameters used in random forest modeling interval division choice; divide feature interval into high correlation and uncertain correlation intervals; select data from two intervals respectively for modeling. Eventually reduce model generalization error, and improve accuracy of prediction.

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