Relevant bibliographies by topics / Selection analysis

Academic literature on the topic 'Selection analysis'

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Published: 4 June 2021
Last updated: 10 February 2022

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Journal articles on the topic "Selection analysis"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Selection analysis"

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Birch, Jonathan George. "Kin selection : a philosophical analysis." Thesis, University of Cambridge, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.648149.

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Mirchandani, Chandru, and Parminder Ghuman. "PERFORMANCE ANALYSIS FOR SYSTEM SELECTION." International Foundation for Telemetering, 2001. http://hdl.handle.net/10150/607568.

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International Telemetering Conference Proceedings / October 22-25, 2001 / Riviera Hotel and Convention Center, Las Vegas, Nevada
The development of unique solutions to telemetry processing using the latest technologies is often fraught with the uncertainty of the system working correctly within the schedule for operational support. This uncertainty can be reduced considerably by analyzing the performance of the system during the development and incremental test stage. This paper describes a method by which the analysis may be carried out during development so that the system will have the capability in the required time for mission support. This paper will show how different system models lend themselves to the requirements, and how the analyses identifies areas of high risk. The paper will also describe a case study whereby these three alternatives to telemetry processing have been used and could have been analyzed so that they would have met the requirements in a timely manner.
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Hiles, James F. "Multi-phase source selection strategy analysis." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2000. http://handle.dtic.mil/100.2/ADA386722.

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Thesis (M.S. in Management) Naval Postgraduate School, Dec. 2000.
"December 2000." Thesis advisor(s): Jeffrey Cuskey, Keith Snider. Includes bibliographical references (p. 111-114). Also available online.
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Hare, Brian K. Dinakarpandian Deendayal. "Feature selection in DNA microarray analysis." Diss., UMK access, 2004.

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Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2004.
"A thesis in computer science." Typescript. Advisor: D. Dinakarpandian. Vita. Title from "catalog record" of the print edition Description based on contents viewed Feb. 24, 2006. Includes bibliographical references (leaves 81-86 ). Online version of the print edition.
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Dimitrakopoulou, Vasiliki. "Bayesian variable selection in cluster analysis." Thesis, University of Kent, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.594195.

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Statistical analysis of data sets of high-dimensionality has met great interest over the past years, with great applications on disciplines such as medicine, nellascience, pattern recognition, image analysis and many others. The vast number of available variables though, contrary to the limited sample size, often mask the cluster structure of the data. It is often that some variables do not help in distinguishing the different clusters in the data; patterns over the samp•.l ed observations are, thus, usually confined to a small subset of variables. We are therefore interested in identifying the variables that best discriminate the sample, simultaneously to recovering the actual cluster structure of the objects under study. With the Markov Chain Monte Carlo methodology being widely established, we investigate the performance of the combined tasks of variable selection and clustering procedure within the Bayesian framework. Motivated by the work of Tadesse et al. (2005), we identify the set of discriminating variables with the use of a latent vector and form the clustering procedure within the finite mixture models methodology. Using Markov chains we draw inference on, not just the set of selected variables and the cluster allocations, but also on the actual number of components: using the f:teversible Jump MCMC sampler (Green, 1995) and a variation of t he SAMS sampler of Dahl (2005). However, sensitivity t o the hyperparameters settings of the covariance structure of the suggested model motivated our interest in an Empirical Bayes procedure to pre-specify the crucial hyper parameters. Further on addressing the problem of II ~----. -- 1 hyperparameters' sensitivity, we suggest several different covariance structures for the mixture components. Developing MATLAB codes for all models introduced in this thesis, we apply and compare the various models suggested on a set of simulated data, as well as on three real data sets; the iris, the crabs and the arthritis data sets.
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Bastola, Jatan, Kenneth E. Findley, and Nathan T. Woodward. "Analysis of contract source selection strategy." Thesis, Monterey, California: Naval Postgraduate School, 2015. http://hdl.handle.net/10945/45810.

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Approved for public release; distribution is unlimited
The Department of Defense (DOD) spends billions acquiring weapons systems, supplies, and services. The contract management process has to be executed diligently to ensure the government is receiving the highest return on investment. The process has six steps, two of which relate to the source selection strategy: solicitation planning and source selection. Once the acquisition team determines whether to use a lowest price technically acceptable (LPTA) or Tradeoff source selection strategy, they evaluate proposals to determine which offer presents the best value to the government. The purpose of this research is to explore potential relationships between the source selection strategy (LPTA or Tradeoff) and resultant contract outcomes. This research uses data collected from contract files and related documentation from two major systems commands (Naval Air Systems Command and Naval Sea Systems Command) to show the implication of the LPTA and Tradeoff source selection strategies. The findings suggest that an LPTA source selection strategy has a significantly shorter lead-time to contract award. The findings should be viewed with caution, however, as the sample size consisted of only six LPTA contracts. This report concludes with two recommendations to improve further research on choosing a source selection strategy and contract outcomes.
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Ni, Xuelei. "New results in detection, estimation, and model selection." Available online, Georgia Institute of Technology, 2006, 2006. http://etd.gatech.edu/theses/available/etd-12042005-190654/.

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Thesis (Ph. D.)--Industrial and Systems Engineering, Georgia Institute of Technology, 2006.
Xiaoming Huo, Committee Chair ; C. F. Jeff Wu, Committee Member ; Brani Vidakovic, Committee Member ; Liang Peng, Committee Member ; Ming Yuan, Committee Member.
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Garrad, Mark, and n/a. "Computer Aided Text Analysis in Personnel Selection." Griffith University. School of Applied Psychology, 2004. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20040408.093133.

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This program of research was aimed at investigating a novel application of computer aided text analysis (CATA). To date, CATA has been used in a wide variety of disciplines, including Psychology, but never in the area of personnel selection. Traditional personnel selection techniques have met with limited success in the prediction of costly training failures for some occupational groups such as pilot and air traffic controller. Accordingly, the overall purpose of this thesis was to assess the validity of linguistic style to select personnel. Several studies were used to examine the structure of language in a personnel selection setting; the relationship between linguistic style and the individual differences dimensions of ability, personality and vocational interests; the validity of linguistic style as a personnel selection tool and the differences in linguistic style across occupational groups. The participants for the studies contained in this thesis consisted of a group of 810 Royal Australian Air Force Pilot, Air Traffic Control and Air Defence Officer trainees. The results partially supported two of the eight hypotheses; the other six hypotheses were supported. The structure of the linguistic style measure was found to be different in this study compared with the structure found in previous research. Linguistic style was found to be unrelated to ability or vocational interests, although some overlap was found between linguistic style and the measure of personality. In terms of personnel selection validity, linguistic style was found to relate to the outcome of training for the occupations of Pilot, Air Traffic Control and Air Defence Officer. Linguistic style also demonstrated incremental validity beyond traditional ability and selection interview measures. The findings are discussed in light of the Five Factor Theory of Personality, and motivational theory and a modified spreading activation network model of semantic memory and knowledge. A general conclusion is drawn that the analysis of linguistic style is a promising new tool in the area of personnel selection.
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Stark, J. Alex. "Statistical model selection techniques for data analysis." Thesis, University of Cambridge, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.390190.

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ALBUQUERQUE, LUIZ FERNANDO FERNANDES DE. "ONLINE ALGORITHMS ANALYSIS FOR SPONSORED LINKS SELECTION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2009. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=16088@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
Links patrocinados são aqueles que aparecem em destaque nos resultados de pesquisas em máquinas de busca na Internet e são grande fonte de receita para seus provedores. Para os anunciantes, que fazem ofertas por palavras-chave para aparecerem em destaque nas consultas dos usuários, são uma oportunidade de divulgação da marca, conquista e manutenção de clientes. Um dos desafios das máquinas de busca neste modelo de negócio é selecionar os anunciantes que serão exibidos a cada consulta de modo a maximizar sua receita em determinado período. Este é um problema tipicamente online, onde a cada consulta é tomada uma decisão sem o conhecimento prévio das próximas consultas. Após uma decisão ser tomada, esta não pode mais ser alterada. Nesta dissertação avaliamos experimentalmente algoritmos propostos na literatura para solução deste problema, comparando-os à solução ótima offline, em simulações com dados sintéticos. Supondo que o conjunto das consultas diárias obedeça a uma determinada distribuição, propomos dois algoritmos baseados em informações estocásticas que são avaliados nos mesmos cenários que os outros algoritmos.
Sponsored links are those that appear highlighted at Internet search engine results. They are responsible for a large amount of their providers’ revenue. To advertisers, that place bids for keywords in large auctions at Internet, these links are the opportunity of brand exposing and achieving more clients. To search engine companies, one of the main challenges in this business model is selecting which advertisers should be allocated to each new query to maximize their total revenue in the end of the day. This is a typical online problem, where for each query is taken a decision without previous knowledge of future queries. Once the decision is taken, it can not be modified anymore. In this work, using synthetically generated data, we do experimental evaluation of three algorithms proposed in the literature for this problem and compare their results with the optimal offline solution. Considering that daily query set obeys some well known distribution, we propose two algorithms based on stochastic information, those are evaluated in the same scenarios of the others.
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Books on the topic "Selection analysis"

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Draper, Norman Richard. Model selection problems. Toronto: University of Toronto, Dept. of Statistics, 1986.

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M, Cheney John, ed. Investments: Analysis, selection, and management. St. Paul: West Pub. Co., 1989.

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Thomas, Norman. Risk analysis and security countermeasure selection. Boca Raton, FL: CRC Press, 2010.

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Thomas, Norman. Risk analysis and security countermeasure selection. Boca Raton, FL: CRC Press, 2010.

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Hiles, James F. Multi-phase source selection strategy analysis. Monterey, Calif: Naval Postgraduate School, 2000.

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Norman, Thomas. Risk analysis and security countermeasure selection. Boca Raton, FL: CRC Press, 2010.

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Norman, Thomas L. Risk analysis and security countermeasure selection. Boca Raton: CRC Press, 2010.

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Subset selection in regression. 2nd ed. Boca Raton: Chapman & Hall/CRC, 2002.

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J, Miller Alan. Subset selection in regression. London [England]: Chapman and Hall, 1990.

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Chih-Ling, Tsai, ed. Regression and time series model selection. Singapore: World Scientific, 1998.

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Book chapters on the topic "Selection analysis"

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Leo, Sabato. "Analysis Selection." In Springer Theses, 57–66. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07929-5_4.

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Westfall, Peter H., and Andrea L. Arias. "Variable Selection." In Understanding Regression Analysis, 273–94. Boca Raton : CRC Press, [2020]: Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9781003025764-11.

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Elgendi, Mohamed. "Feature Selection." In PPG Signal Analysis, 165–93. Boca Raton : Taylor & Francis, [2018]: CRC Press, 2020. http://dx.doi.org/10.1201/9780429449581-8.

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Huang, Xiaoxia. "Probabilistic Portfolio Selection." In Portfolio Analysis, 11–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11214-0_2.

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Huang, Xiaoxia. "Credibilistic Portfolio Selection." In Portfolio Analysis, 61–115. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11214-0_3.

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Huang, Xiaoxia. "Uncertain Portfolio Selection." In Portfolio Analysis, 117–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11214-0_4.

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Härdle, Wolfgang Karl, and Léopold Simar. "Variable Selection." In Applied Multivariate Statistical Analysis, 261–83. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26006-4_9.

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Härdle, Wolfgang Karl, and Léopold Simar. "Variable Selection." In Applied Multivariate Statistical Analysis, 281–304. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-45171-7_9.

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Brannick, Michael T., Kenneth Pearlman, and Juan I. Sanchez. "Work Analysis." In Handbook of Employee Selection, 134–61. Second edition. | New York, NY : Routledge, 2017.: Routledge, 2017. http://dx.doi.org/10.4324/9781315690193-6.

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Gillberg, Dag. "Analysis: Event Selection." In Discovery of Single Top Quark Production, 35–60. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-7799-1_5.

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Conference papers on the topic "Selection analysis"

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Nguyen, Margaret B., and Kristy Putnam. "Spatial Analysis of Asthma Emergency Department Visits in California." In Selection of Abstracts From NCE 2016. American Academy of Pediatrics, 2018. http://dx.doi.org/10.1542/peds.141.1_meetingabstract.358.

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Nakariyakul, Songyot, and David P. Casasent. "Improved forward floating selection algorithm for feature subset selection." In 2008 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR). IEEE, 2008. http://dx.doi.org/10.1109/icwapr.2008.4635885.

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Petersen, Tara L., and Karen Marcdante. "Analysis of Education Surrounding Cost Effective Medicine on Bedside Rounds." In Selection of Abstracts From NCE 2016. American Academy of Pediatrics, 2018. http://dx.doi.org/10.1542/peds.141.1_meetingabstract.311.

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Zeng, Ziqian, Zimevg Wu, and Wing W. Y. Ng. "Projection selection hashing." In 2015 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR). IEEE, 2015. http://dx.doi.org/10.1109/icwapr.2015.7295948.

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Glaun, Gabriel D., Chad A. Selph, Robert M. Pride, Denise Lopez, Mark A. Birnbaum, and Jonathan H. Phillips. "Novel Radiographic Analysis of Guided Growth for Knee Flexion Contractures: A Retrospective Analysis of Anterior-Distal Hemiepiphysiodesis of the Femur via Utilization of Blumensaat's Line." In Selection of Abstracts From NCE 2015. American Academy of Pediatrics, 2017. http://dx.doi.org/10.1542/peds.140.1_meetingabstract.113.

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Goel, Tushar, and Nielen Stander. "Influence of Selection Criterion on RBFs Network Topology Selection for Crashworthiness Optimization." In 12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2008. http://dx.doi.org/10.2514/6.2008-5997.

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Spooner, Stephen A., Sarah Shields, Judith W. Dexheimer, Cecilia M. Mahdi, Philip Hagedorn, and Thomas Minich. "Weight Entry Error Detection: A Web Service for Real-time Statistical Analysis." In Selection of Abstracts From NCE 2016. American Academy of Pediatrics, 2018. http://dx.doi.org/10.1542/peds.141.1_meetingabstract.21.

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Freedman, Stephen, Silviu Grisaru, Susan Samuel, Lisa Hartling, Phillip Tarr, and David Schnadower. "Shiga Toxin-producing Escherichia Coli, Hydration Status, and Outcomes: A Meta-analysis." In Selection of Abstracts From NCE 2016. American Academy of Pediatrics, 2018. http://dx.doi.org/10.1542/peds.141.1_meetingabstract.168.

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Duller, Christine. "Model selection for logistic regression models." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2012: International Conference of Numerical Analysis and Applied Mathematics. AIP, 2012. http://dx.doi.org/10.1063/1.4756152.

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McCarty, Thomas R., Thomas j. Abramo, Robert Maxson, Gregory Albert, Jonathan Orsborn, Mallikarjuna Rettiganti, Marie Saylors, and Amanda Hollingsworth. "Hypothermia as an Outcome Predictor Tool in Pediatric Trauma: A Propensity Matched Analysis." In Selection of Abstracts From NCE 2016. American Academy of Pediatrics, 2018. http://dx.doi.org/10.1542/peds.141.1_meetingabstract.347.

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Reports on the topic "Selection analysis"

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VENKATARAMAN PASUPATHI. WASTE PACKAGE MATERIALS SELECTION ANALYSIS. Office of Scientific and Technical Information (OSTI), December 1997. http://dx.doi.org/10.2172/778855.

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Bastola, Jatan, Kenneth E. Findley, and Nathan T. Woodward. Analysis of Contract Source Selection Strategy. Fort Belvoir, VA: Defense Technical Information Center, July 2015. http://dx.doi.org/10.21236/ad1016672.

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Bastola, Jatan, Kenneth E. Findley, and Nathan T. Woodward. Analysis of Contract Source Selection Strategy. Fort Belvoir, VA: Defense Technical Information Center, June 2015. http://dx.doi.org/10.21236/ada632334.

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Jay, Joni. Media Selection Analysis for 19K BNCOC. Fort Belvoir, VA: Defense Technical Information Center, June 1986. http://dx.doi.org/10.21236/ada169670.

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Sisto, A., and C. Kamath. Ensemble Feature Selection in Scientific Data Analysis. Office of Scientific and Technical Information (OSTI), September 2013. http://dx.doi.org/10.2172/1097710.

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Hunter, David R., and Eugene F. Burke. Meta Analysis of Aircraft Pilot Selection Measures. Fort Belvoir, VA: Defense Technical Information Center, June 1992. http://dx.doi.org/10.21236/ada253387.

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Bloom, David, and Christopher Cavanagh. An Analysis of the Selection of Arbitrators. Cambridge, MA: National Bureau of Economic Research, June 1986. http://dx.doi.org/10.3386/w1938.

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Antypas, Katie, John Shalf, and Harvey Wasserman. NERSC-6 Workload Analysis and Benchmark Selection Process. Office of Scientific and Technical Information (OSTI), August 2008. http://dx.doi.org/10.2172/938789.

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Lamoureux, Jacques, Michael Murrow, and Clinton Walls. Relationship of Source Selection Methods to Contract Outcomes: An Analysis of Air Force Source Selection. Fort Belvoir, VA: Defense Technical Information Center, December 2015. http://dx.doi.org/10.21236/ada632445.

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Young, M., and R. Vilhauer. Sri Lanka Wind Farm Analysis and Site Selection Assistance. Office of Scientific and Technical Information (OSTI), August 2003. http://dx.doi.org/10.2172/15004300.

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