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Journal articles on the topic 'Selection analysis'

To see the other types of publications on this topic, follow the link: Selection analysis.

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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1

Horita, Hiroki, and Junji Noguchi. "Fairness Analysis in Goal-Oriented Requirements Selection." International Journal of Trade, Economics and Finance 11, no. 4 (August 2020): 77–82. http://dx.doi.org/10.18178/ijtef.2020.11.4.670.

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2

Bing-Xin Du, Bing-Xin Du. "Topic Analysis in LDA Based on Keywords Selection." 電腦學刊 32, no. 4 (August 2021): 001–12. http://dx.doi.org/10.53106/199115992021083204001.

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3

Moravčíková, Nina, Radovan Kasarda, Luboš Vostrý, Zuzana Krupová, Emil Krupa, Kristína Lehocká, Barbora Olšanská, et al. "Analysis of selection signatures in the beef cattle genome." Czech Journal of Animal Science 64, No. 12 (December 22, 2019): 491–503. http://dx.doi.org/10.17221/226/2019-cjas.

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This study aimed to evaluate the impact of selection on the genome structure of beef cattle through identification of selection signatures reflecting the breeding standard of each breed and to discover potential functional genetic variants to improve performance traits. Genotyping data of six beef breeds (Aberdeen Angus, Hereford, Limousin, Charolais, Piedmontese and Romagnola) were used to perform genome-wide scans for selection signatures. The approaches applied were based on an assumption that selection leads to linkage disequilibrium or to a decrease of genetic variability in genomic regions containing genotypes connected with favourable phenotypes. Thus, the selection signatures were analysed based on Wright’s F<sub>ST</sub> index, distribution of runs of homozygosity segments in the beef genome and determination of linkage disequilibrium variability between breeds. The number and length of detected selection signals were different depending on the breeds and methodological approaches. As expected due to the breeding goals of analysed breeds, common signals were located on autosomes 2, 6, 7, 13 and 20 close to the genes associated with coat colour (KIT, KDR), muscle development (GDF9, GHRH, GHR), double muscling (MSTN), meat tenderness (CAST) and intramuscular fat content (SCD). But, across the genomes of analysed breeds, unique selection signals were found as well. The subsequent analysis of those single nucleotide polymorphism markers can be beneficial for the genetic progress of studied breeds in future.

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4

R, Dr Prema. "Feature Selection for Gene Expression Data Analysis – A Review." International Journal of Psychosocial Rehabilitation 24, no. 5 (May 25, 2020): 6955–64. http://dx.doi.org/10.37200/ijpr/v24i5/pr2020695.

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5

Monnahan, Patrick J., Jack Colicchio, and John K. Kelly. "A genomic selection component analysis characterizes migration-selection balance." Evolution 69, no. 7 (July 2015): 1713–27. http://dx.doi.org/10.1111/evo.12698.

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6

Liu, Hailiang, and Peter Markowich. "Selection dynamics for deep neural networks." Journal of Differential Equations 269, no. 12 (December 2020): 11540–74. http://dx.doi.org/10.1016/j.jde.2020.08.041.

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7

Bailey, Jeffery V., and Robert D. Arnott. "Cluster Analysis and Manager Selection." Financial Analysts Journal 42, no. 6 (November 1986): 20–28. http://dx.doi.org/10.2469/faj.v42.n6.20.

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8

Dean, Nema, and Adrian E. Raftery. "Latent class analysis variable selection." Annals of the Institute of Statistical Mathematics 62, no. 1 (July 24, 2009): 11–35. http://dx.doi.org/10.1007/s10463-009-0258-9.

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9

Goodnight, Charles J. "Contextual analysis and group selection." Behavioral and Brain Sciences 17, no. 4 (December 1994): 622. http://dx.doi.org/10.1017/s0140525x00036268.

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10

Gabriel and Marjorie Laury. "Critical Analysis of Seating Selection." IEEE Engineering Management Review 15, no. 2 (June 1987): 18–24. http://dx.doi.org/10.1109/emr.1987.4306277.

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11

Edwards, S. V. "Natural selection and phylogenetic analysis." Proceedings of the National Academy of Sciences 106, no. 22 (May 26, 2009): 8799–800. http://dx.doi.org/10.1073/pnas.0904103106.

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12

Guegan, B., J. Hardin, J. Stevens, and M. Williams. "Model selection for amplitude analysis." Journal of Instrumentation 10, no. 09 (September 1, 2015): P09002. http://dx.doi.org/10.1088/1748-0221/10/09/p09002.

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13

魏, 学利. "Preliminary Analysis of Road Selection." Advances in Geosciences 07, no. 01 (2017): 39–49. http://dx.doi.org/10.12677/ag.2017.71005.

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14

HUNTLEY, MARY I., and MARION G. ANEMA. "NURSING ISSUES: SELECTION AND ANALYSIS." Nurse Educator 15, no. 1 (January 1990): 24–30. http://dx.doi.org/10.1097/00006223-199001000-00012.

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15

Novotny, Timothy J., and Lyman L. Mcdonald. "Model selection using discriminant analysis." Journal of Applied Statistics 13, no. 2 (January 1986): 159–65. http://dx.doi.org/10.1080/02664768600000024.

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16

Beitz, Janice M. "Statistical Test Selection and Analysis." Journal of Wound, Ostomy and Continence Nursing 35, no. 6 (November 2008): 561–68. http://dx.doi.org/10.1097/01.won.0000341468.56438.4a.

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17

Bayarri, M. J., and M. H. DeGroot. "Bayesian Analysis of Selection Models." Statistician 36, no. 2/3 (1987): 137. http://dx.doi.org/10.2307/2348506.

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18

Chang, Xiaocheng, Jianhua Yang, and Shengjun Zhu. "Comprehensive analysis of conductor selection." IOP Conference Series: Materials Science and Engineering 382 (July 2018): 022069. http://dx.doi.org/10.1088/1757-899x/382/2/022069.

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19

Earnshaw, Eugene. "Group selection and contextual analysis." Synthese 192, no. 1 (October 17, 2014): 305–16. http://dx.doi.org/10.1007/s11229-014-0569-0.

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20

Oettinger, Peter E., Ruth E. Shefer, Dan L. Birx, and Michael C. Green. "Photoelectron sources: Selection and analysis." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 272, no. 1-2 (October 1988): 264–67. http://dx.doi.org/10.1016/0168-9002(88)90234-3.

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21

H Alhamlan, Nasir. "Introducing Nora Torque Selection Analysis." Acta Scientific Dental Scienecs 5, no. 8 (July 21, 2021): 78–84. http://dx.doi.org/10.31080/asds.2021.05.1172.

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22

Xu, Peng, Ahmad Ghasemloonia, and Qiao Sun. "Automatic band selection algorithm for envelope analysis." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 233, no. 5 (May 29, 2018): 1641–54. http://dx.doi.org/10.1177/0954406218776342.

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Envelope analysis has been widely used to detect early stage faults of rolling element bearings. The primary initial step of envelope analysis is the proper selection of the resonance band for demodulation. Current band selection methods, such as wide band selection, “power spectral density” comparison, and selecting the accelerometer resonance band have limitations such as disturbance of the wide band, the need for a healthy signal for comparison, and the implementation of specialized sensors. In this study, an enhanced method of resonance band selection for envelope analysis was developed. The developed method implements high-pass filtering and “time synchronous averaging” to remove dominant speed-dependent (nonsynchronous and synchronous) spectral contents of a vibration signal. Wavelet packet transform and “root mean square” were then applied to determine the energy distribution of the residual signal. The band with the highest energy (resonance band) was selected for envelope analysis. An experimental study was designed for cross-validation of the developed method. The developed method in this study is more practical than current band selection methods and has no special requirement for sensors. The developed algorithm can be implemented as a processing algorithm in a commercial vibration analyzer, which enhances its ability in early-stage bearing fault detection.

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23

Dean, B. V., and M. J. Schniederjans. "A multiple objective selection methodology for strategic industry selection analysis." IEEE Transactions on Engineering Management 38, no. 1 (1991): 53–62. http://dx.doi.org/10.1109/17.65760.

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24

Flanagan, Sarah P., and Adam G. Jones. "Identifying signatures of sexual selection using genomewide selection components analysis." Ecology and Evolution 5, no. 13 (June 19, 2015): 2722–44. http://dx.doi.org/10.1002/ece3.1546.

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25

Strothmann, Molly, and Karen Rupp-Serrano. "A Comparative Analysis of Evidence-based Selection, Professional Selection, and Selection by Approval Plan." Library Resources & Technical Services 64, no. 1 (2020): 15–25. http://dx.doi.org/10.5860/lrts.61n1.15.

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26

Strothmann, Molly, and Karen Rupp-Serrano. "A Comparative Analysis of Evidence-based Selection, Professional Selection, and Selection by Approval Plan." Library Resources & Technical Services 64, no. 1 (2020): 15–25. http://dx.doi.org/10.5860/lrts.64n1.15.

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27

Avron, Haim, and Christos Boutsidis. "Faster Subset Selection for Matrices and Applications." SIAM Journal on Matrix Analysis and Applications 34, no. 4 (January 2013): 1464–99. http://dx.doi.org/10.1137/120867287.

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28

Hwang, Shiuh-Nan, Chin-Tsai Lin, and Wang-Ching Chuang. "Stock selection using data envelopment analysis-discriminant analysis." Journal of Information and Optimization Sciences 28, no. 1 (January 2007): 33–50. http://dx.doi.org/10.1080/02522667.2007.10699727.

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29

MUKHERJEE, SACH, and STEPHEN J. ROBERTS. "A THEORETICAL ANALYSIS OF THE SELECTION OF DIFFERENTIALLY EXPRESSED GENES." Journal of Bioinformatics and Computational Biology 03, no. 03 (June 2005): 627–43. http://dx.doi.org/10.1142/s0219720005001211.

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A great deal of recent research has focused on the challenging task of selecting differentially expressed genes from microarray data ("gene selection"). Numerous gene selection algorithms have been proposed in the literature, but it is often unclear exactly how these algorithms respond to conditions like small sample sizes or differing variances. Choosing an appropriate algorithm can therefore be difficult in many cases. In this paper we propose a theoretical analysis of gene selection, in which the probability of successfully selecting differentially expressed genes, using a given ranking function, is explicitly calculated in terms of population parameters. The theory developed is applicable to any ranking function which has a known sampling distribution, or one which can be approximated analytically. In contrast to methods based on simulation, the approach presented here is computationally efficient and can be used to examine the behavior of gene selection algorithms under a wide variety of conditions, even when the number of genes involved runs into the tens of thousands. The utility of our approach is illustrated by comparing three widely-used gene selection methods.

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30

Whaiduzzaman, Md, Abdullah Gani, Nor Badrul Anuar, Muhammad Shiraz, Mohammad Nazmul Haque, and Israat Tanzeena Haque. "Cloud Service Selection Using Multicriteria Decision Analysis." Scientific World Journal 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/459375.

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Cloud computing (CC) has recently been receiving tremendous attention from the IT industry and academic researchers. CC leverages its unique services to cloud customers in a pay-as-you-go, anytime, anywhere manner. Cloud services provide dynamically scalable services through the Internet on demand. Therefore, service provisioning plays a key role in CC. The cloud customer must be able to select appropriate services according to his or her needs. Several approaches have been proposed to solve the service selection problem, including multicriteria decision analysis (MCDA). MCDA enables the user to choose from among a number of available choices. In this paper, we analyze the application of MCDA to service selection in CC. We identify and synthesize several MCDA techniques and provide a comprehensive analysis of this technology for general readers. In addition, we present a taxonomy derived from a survey of the current literature. Finally, we highlight several state-of-the-art practical aspects of MCDA implementation in cloud computing service selection. The contributions of this study are four-fold: (a) focusing on the state-of-the-art MCDA techniques, (b) highlighting the comparative analysis and suitability of several MCDA methods, (c) presenting a taxonomy through extensive literature review, and (d) analyzing and summarizing the cloud computing service selections in different scenarios.

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31

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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32

Wang, Longfei, Qin Yang, Adriana Jaimes, Tianyu Wang, Hendrik Strobelt, Jenny Chen, and Piotr Sliz. "MightyScreen: An Open-Source Visualization Application for Screening Data Analysis." SLAS DISCOVERY: Advancing the Science of Drug Discovery 23, no. 2 (September 22, 2017): 218–23. http://dx.doi.org/10.1177/2472555217731983.

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Screening is a methodology widely used in biological and biomedical research. There are numerous visualization methods to validate screening data quality but very few visualization applications capable of hit selection. Here, we present MightyScreen ( mightyscreen.net ), a novel web-based application designed for visual data evaluation as well as visual hit selection. We believe MightyScreen is an intuitive and interactive addition to conventional hit selection methods. We also provide study cases showing how MightyScreen is used to visually explore screening data and make hit selections.

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33

ZHOU, XIN, and K. Z. MAO. "REGULARIZATION NETWORK-BASED GENE SELECTION FOR MICROARRAY DATA ANALYSIS." International Journal of Neural Systems 16, no. 05 (October 2006): 341–52. http://dx.doi.org/10.1142/s0129065706000743.

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Microarray data contains a large number of genes (usually more than 1000) and a relatively small number of samples (usually fewer than 100). This presents problems to discriminant analysis of microarray data. One way to alleviate the problem is to reduce dimensionality of data by selecting important genes to the discriminant problem. Gene selection can be cast as a feature selection problem in the context of pattern classification. Feature selection approaches are broadly grouped into filter methods and wrapper methods. The wrapper method outperforms the filter method but at the cost of more intensive computation. In the present study, we proposed a wrapper-like gene selection algorithm based on the Regularization Network. Compared with classical wrapper method, the computational costs in our gene selection algorithm is significantly reduced, because the evaluation criterion we proposed does not demand repeated training in the leave-one-out procedure.

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34

Higham, Desmond J. "Trust Region Algorithms and Timestep Selection." SIAM Journal on Numerical Analysis 37, no. 1 (January 1999): 194–210. http://dx.doi.org/10.1137/s0036142998335972.

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35

Kitajima, Mitsuro, Kensuke Baba, and Toshiro Minami. "An Evaluation of Book Selection in a University Library by Loan Record Analysis." International Journal of Information and Education Technology 5, no. 10 (2015): 728–31. http://dx.doi.org/10.7763/ijiet.2015.v5.601.

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36

Corus, Dogan, Andrei Lissovoi, Pietro S. Oliveto, and Carsten Witt. "On Steady-State Evolutionary Algorithms and Selective Pressure: Why Inverse Rank-Based Allocation of Reproductive Trials Is Best." ACM Transactions on Evolutionary Learning and Optimization 1, no. 1 (April 27, 2021): 1–38. http://dx.doi.org/10.1145/3427474.

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We analyse the impact of the selective pressure for the global optimisation capabilities of steady-state evolutionary algorithms (EAs). For the standard bimodal benchmark function TwoMax , we rigorously prove that using uniform parent selection leads to exponential runtimes with high probability to locate both optima for the standard ( +1) EA and ( +1) RLS with any polynomial population sizes. However, we prove that selecting the worst individual as parent leads to efficient global optimisation with overwhelming probability for reasonable population sizes. Since always selecting the worst individual may have detrimental effects for escaping from local optima, we consider the performance of stochastic parent selection operators with low selective pressure for a function class called TruncatedTwoMax, where one slope is shorter than the other. An experimental analysis shows that the EAs equipped with inverse tournament selection, where the loser is selected for reproduction and small tournament sizes, globally optimise TwoMax efficiently and effectively escape from local optima of TruncatedTwoMax with high probability. Thus, they identify both optima efficiently while uniform (or stronger) selection fails in theory and in practice. We then show the power of inverse selection on function classes from the literature where populations are essential by providing rigorous proofs or experimental evidence that it outperforms uniform selection equipped with or without a restart strategy. We conclude the article by confirming our theoretical insights with an empirical analysis of the different selective pressures on standard benchmarks of the classical MaxSat and multidimensional knapsack problems.

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37

Chu, Liang-Ju, and Chien-Hao Huang. "Generalized selection theorems without convexity." Nonlinear Analysis: Theory, Methods & Applications 73, no. 10 (November 2010): 3224–31. http://dx.doi.org/10.1016/j.na.2010.07.002.

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38

Mao, Jian-feng. "A New Continuous Selection Theorem." Journal of Mathematical Analysis and Applications 222, no. 2 (June 1998): 585–93. http://dx.doi.org/10.1006/jmaa.1998.5927.

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39

Benelmechaiekh, H., and M. Oudadess. "Some Selection Theorems without Convexity." Journal of Mathematical Analysis and Applications 195, no. 2 (October 1995): 614–18. http://dx.doi.org/10.1006/jmaa.1995.1377.

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40

Goodnight, Charles. "ON MULTILEVEL SELECTION AND KIN SELECTION: CONTEXTUAL ANALYSIS MEETS DIRECT FITNESS." Evolution 67, no. 6 (November 8, 2012): 1539–48. http://dx.doi.org/10.1111/j.1558-5646.2012.01821.x.

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41

Oaksford, Mike, and Nick Chater. "A rational analysis of the selection task as optimal data selection." Psychological Review 101, no. 4 (1994): 608–31. http://dx.doi.org/10.1037/0033-295x.101.4.608.

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42

Łazęcka, Małgorzata, and Jan Mielniczuk. "Analysis of Information-Based Nonparametric Variable Selection Criteria." Entropy 22, no. 9 (August 31, 2020): 974. http://dx.doi.org/10.3390/e22090974.

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We consider a nonparametric Generative Tree Model and discuss a problem of selecting active predictors for the response in such scenario. We investigated two popular information-based selection criteria: Conditional Infomax Feature Extraction (CIFE) and Joint Mutual information (JMI), which are both derived as approximations of Conditional Mutual Information (CMI) criterion. We show that both criteria CIFE and JMI may exhibit different behavior from CMI, resulting in different orders in which predictors are chosen in variable selection process. Explicit formulae for CMI and its two approximations in the generative tree model are obtained. As a byproduct, we establish expressions for an entropy of a multivariate gaussian mixture and its mutual information with mixing distribution.

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43

Xie, Xuming. "Rigorous results in selection of steady needle crystals." Journal of Differential Equations 197, no. 2 (March 2004): 349–426. http://dx.doi.org/10.1016/s0022-0396(03)00184-0.

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44

Jayne, J. E., J. Orihuela, A. J. Pallares, and G. Vera. "σ-Fragmentability of Multivalued Maps and Selection Theorems." Journal of Functional Analysis 117, no. 2 (November 1993): 243–73. http://dx.doi.org/10.1006/jfan.1993.1127.

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45

Ye, Xin, Jianlin Xia, and Lexing Ying. "Analytical Low-Rank Compression via Proxy Point Selection." SIAM Journal on Matrix Analysis and Applications 41, no. 3 (January 2020): 1059–85. http://dx.doi.org/10.1137/19m1247838.

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46

Li, Cong Bo, Fei Liu, Qi Feng Wang, and Cai Zhen Li. "AHP Based SWOT Analysis for Green Manufacturing Strategy Selection." Key Engineering Materials 431-432 (March 2010): 249–52. http://dx.doi.org/10.4028/www.scientific.net/kem.431-432.249.

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Selecting an appropriate strategy is very important to the Green Manufacturing implementation. Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis is a commonly used tool for strategic management. Here SWOT analysis in combination with AHP is used to the selection of GM strategy. The strategic factors system of SWOT analysis for GM strategy is constructed by analyzing internal and external environments of the enterprise. Then AHP based SWOT analysis is introduced in detail. A case study demonstrates the application of AHP based SWOT analysis in GM strategy selection and shows its feasibility and validity.

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47

Munita, Casimiro S., Lúcia P. Barroso, and Paulo M. S. Oliveira. "Variable selection study using Procrustes analysis." Open Journal of Archaeometry 1, no. 1 (December 20, 2013): 7. http://dx.doi.org/10.4081/arc.2013.e7.

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Several analytical techniques are often used in archaeometric studies, and when used in combination, these techniques can be used to assess 30 or more elements. Multivariate statistical methods are frequently used to interpret archaeometric data, but their applications can be problematic or difficult to interpret due to the large number of variables. In general, the analyst first measures several variables, many of which may be found to be uninformative, this is naturally very time consuming and expensive. In subsequent studies the analyst may wish to measure fewer variables while attempting to minimize the loss of essential information. Such multidimensional data sets must be closely examined to draw useful information. This paper aims to describe and illustrate a stopping rule for the identification of redundant variables, and the selection of variables subsets, preserving multivariate data structure using Procrustes analysis, selecting those variables that are in some senses adequate for discrimination purposes. We provide an illustrative example of the procedure using a data set of 40 samples in which were determined the concentration of As, Ce, Cr, Eu, Fe, Hf, La, Na, Nd, Sc, Sm, Th, and U obtained via instrumental neutron activation analysis (INAA) on archaeological ceramic samples. The results showed that for this data set, only eight variables (As, Cr, Fe, Hf, La, Nd, Sm, and Th) are required to interpret the data without substantial loss information.

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48

Ngui, Wai Keng, M. Salman Leong, Lim Meng Hee, and Ahmed M. Abdelrhman. "Wavelet Analysis: Mother Wavelet Selection Methods." Applied Mechanics and Materials 393 (September 2013): 953–58. http://dx.doi.org/10.4028/www.scientific.net/amm.393.953.

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Wavelet analysis, being a popular time-frequency analysis method has been applied in various fields to analyze a wide range of signals covering biological signals, vibration signals, acoustic and ultrasonic signals, to name a few. With the capability to provide both time and frequency domains information, wavelet analysis is mainly for time-frequency analysis of signals, signal compression, signal denoising, singularity analysis and features extraction. The main challenge in using wavelet transform is to select the most optimum mother wavelet for the given tasks, as different mother wavelet applied on to the same signal may produces different results. This paper reviews on the mother wavelet selection methods with particular emphasis on the quantitative approaches. A brief description of the proposed new technique to determine the optimum mother wavelet specifically for machinery faults diagnosis is also presented in this paper.

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49

Rea, Alethea, Bill Rea, and Libin Yang. "Stock selection with principal component analysis." Journal of Investment Strategies 5, no. 2 (March 2016): 35–55. http://dx.doi.org/10.21314/jois.2016.067.

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50

Carrico, C. S., S. M. Hogan, R. G. Dyson, and A. D. Athanassopoulos. "Data Envelopment Analysis and University Selection." Journal of the Operational Research Society 48, no. 12 (December 1997): 1163. http://dx.doi.org/10.2307/3010747.

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