Literatura académica sobre el tema "CLUSTER VARIANCE"
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Artículos de revistas sobre el tema "CLUSTER VARIANCE"
Gregg, Mary, Somnath Datta y Doug Lorenz. "Variance estimation in tests of clustered categorical data with informative cluster size". Statistical Methods in Medical Research 29, n.º 11 (8 de junio de 2020): 3396–408. http://dx.doi.org/10.1177/0962280220928572.
Texto completoSankaran, M., M. R. Dinesh, D. C. S. Gowda y R. Venugopalan. "Genetic analysis in mango (Mangifera indica L.) based on fruit characteristics of 400 genotypes". Journal of Horticultural Sciences 15, n.º 2 (31 de diciembre de 2020): 161–72. http://dx.doi.org/10.24154/jhs.2020.v15i02.007.
Texto completoSankaran, M., M. R. Dinesh, D. C. S. Gowda y R. Venugopalan. "Genetic analysis in mango (Mangifera indica L.) based on fruit characteristics of 400 genotypes". Journal of Horticultural Sciences 15, n.º 2 (31 de diciembre de 2020): 161–72. http://dx.doi.org/10.24154/jhs.v15i2.944.
Texto completoYasmeen, Uzma, Muhammad Noor-ul-Amin y Muhammad Hanif. "Variance estimation in stratified adaptive cluster sampling". Statistics in Transition New Series 23, n.º 1 (1 de marzo de 2022): 173–84. http://dx.doi.org/10.2478/stattrans-2022-0010.
Texto completoVeenman, C. J., M. J. T. Reinders y E. Backer. "A maximum variance cluster algorithm". IEEE Transactions on Pattern Analysis and Machine Intelligence 24, n.º 9 (septiembre de 2002): 1273–80. http://dx.doi.org/10.1109/tpami.2002.1033218.
Texto completoA.K, et al., Shukla. "Variance Function in Cluster Sampling". International Journal of Computational and Theoretical Statistics 2, n.º 1 (1 de mayo de 2015): 25–30. http://dx.doi.org/10.12785/ijcts/020103.
Texto completoTerry, L. Irene y Gloria DeGrandi-Hoffman. "MONITORING WESTERN FLOWER THRIPS (THYSANOPTERA: THRIPIDAE) IN “GRANNY SMITH” APPLE BLOSSOM CLUSTERS". Canadian Entomologist 120, n.º 11 (noviembre de 1988): 1003–16. http://dx.doi.org/10.4039/ent1201003-11.
Texto completoVostrá Vydrová, Hana y Zuzana Novotná. "Evaluation of disparities in living standards of regions of the Czech Republic". Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 60, n.º 4 (2012): 407–14. http://dx.doi.org/10.11118/actaun201260040407.
Texto completoScott, JoAnna M., Allan deCamp, Michal Juraska, Michael P. Fay y Peter B. Gilbert. "Finite-sample corrected generalized estimating equation of population average treatment effects in stepped wedge cluster randomized trials". Statistical Methods in Medical Research 26, n.º 2 (29 de septiembre de 2014): 583–97. http://dx.doi.org/10.1177/0962280214552092.
Texto completoGrilli, Leonardo y Carla Rampichini. "The Role of Sample Cluster Means in Multilevel Models". Methodology 7, n.º 4 (1 de agosto de 2011): 121–33. http://dx.doi.org/10.1027/1614-2241/a000030.
Texto completoTesis sobre el tema "CLUSTER VARIANCE"
Akdemir, Deniz. "Components Of Response Variance For Cluster Samples". Master's thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/1206044/index.pdf.
Texto completoYou, Zhiying. "Power and sample size of cluster randomized trials". Thesis, Birmingham, Ala. : University of Alabama at Birmingham, 2008. https://www.mhsl.uab.edu/dt/2009r/you.pdf.
Texto completoPark, Misook. "Design and Analysis Methods for Cluster Randomized Trials with Pair-Matching on Baseline Outcome: Reduction of Treatment Effect Variance". VCU Scholars Compass, 2006. http://hdl.handle.net/10156/2195.
Texto completoDunning, Allison. "Comparing Bootstrap and Jackknife Variance Estimation Methods for Area Under the ROC Curve Using One-Stage Cluster Survey Data". VCU Scholars Compass, 2009. http://scholarscompass.vcu.edu/etd/1849.
Texto completoRandriatsiferana, Rivo Sitraka A. "Optimisation énergétique des protocoles de communication des réseaux de capteurs sans fil". Thesis, La Réunion, 2014. http://www.theses.fr/2014LARE0019/document.
Texto completoTo increase the lifetime of wireless sensor networks, a solution is to improve the energy efficiency of the communication's protocol. The grouping of nodes in the wireless sensor network clustering is one of the best methods. This thesis proposes several improvements by changing the settings of the reference protocol LEACH. To improve the energy distribution of "cluster-heads", we propose two centralized clustering protocols LEACH and k-optimized version k-LEACH-VAR. A distributed algorithm, called e-LEACH, is proposed to reduce the periodic exchange of information between the nodes and the base station during the election of "cluster-heads". Moreover, the concept of energy balance is introduced in metric election to avoid overloading nodes. Then we presented a decentralized version of k-LEACH, which in addition to the previous objectives, integrates the overall energy consumption of the network. This protocol, called k-LEACH-C2D, also aims to promote the scalability of the network. To reinforce the autonomy and networks, both routing protocols "multi-hop" probability, denoted CB-RSM and FRSM build elementary paths between the "cluster-heads" and elected the base station. The protocol, CB-RSM, forms a hierarchy of "cluster-heads" during the training phase clusters, with an emphasis on self-scheduling and self-organization between "cluster-heads" to make the networks more scalable. These protocols are based on the basic idea that the nodes have the highest residual energy and lower variance of energy consumption become "cluster-head". We see the central role of consumption of the node in our proposals. This point will be the last part of this thesis. We propose a methodology to characterize experimentally the consumption of a node. The objectives are to better understand the consumption for different sequences of the node status. In the end, we propose a global model of the consumption of the node
Diaz, Acosta Beatriz. "Experiments in Image Segmentation for Automatic US License Plate Recognition". Thesis, Virginia Tech, 2004. http://hdl.handle.net/10919/9988.
Texto completoMaster of Science
Piana, Clause Fátima de Brum. "Regionalização para o cultivo do feijão no Rio Grande do Sul com base na interação genótipo x ambiente". Universidade Federal de Pelotas, 2009. http://repositorio.ufpel.edu.br/handle/ri/2081.
Texto completoIn Brazil, common bean (Phaseolus vulgaris L.) is cultivated in a range of ecologically differentiated environments. For being a culture highly influenced by the environment variation, its average productivity in the Country is unstable and low. An origin of this variation of productivity is the genotype x environment interaction, which has been one of the largest impediments for obtaining genotypes that maintain consistently high yield in the growing environments. The methods proposed for the exploration of the genotype x environment interaction are directed to the stability of the yield of the genotypes or to the regionalization of the growing locations. Most of the common bean genotypes registered for cultivation in Rio Grande do Sul evidences yield instability. The present research explored data from Rio Grande do Sul Common Bean State Trial ("Ensaio Estadual de Feijão" - EEF), executed at 24 locations in the period from 1987/88 to 1994/95, with considerable variation of genotypes and locations among those years. This research had two main objectives: (1) to evaluate the magnitude and the nature of the genotype x environment interaction and (2) to identify possible stratification of the growing region of common bean in the State in sub-regions inside of which the genotypes have stable relative performance. The inferences about the components of the interaction genotype x environment were proceeded by the joint analysis of each one of the eight years and the analyses of two subsets of four years and of the set of eight years. Because of the intent of obtaining a long time regionalization, general for the growing location of the Rio Grande do Sul and for any collection of beans genotypes, the factors year, location and genotype were considered random. The maximum likelihood and the generalized minimum squares methods were used. This approach allowed taking into account the incomplete and unbalanced structure of the data and the heterogeneity of variance of the experimental error. The results of the annual analyses revealed high significance of the component of the interaction genotype x location in all of the years, indicating that the relative performance of the genotypes varies among locations. This interaction was also revealed significant in the analysis of the eight years, but was not significant in the analyses of the two subsets four years. In these three joint analyses of years the triple interaction genotype x location x year was highly significant. The indication of heterogeneous performance of the genotypes among the locations and the possibility that the pattern of performance have some consistence along the years justified the attempt to the grouping of the locations. Cluster analyses were performed for each one of the eight years and for the set of eight years by the method of Sokal and Michener, that uses the Euclidean distance as similarity measure. The cluster analysis of the set of eight years constituted subregions that are generally incoherent with the sub-regions formed by the annual analyses that, by they turn, were inconsistent amongst themselves. This incoherence and inconsistency of groupings disabled the characterization of a division of the State for the regionalization of the indication of cultivars. It should be observed, however, that these evidences might have been influenced by the considerable alterations of the genotypes and of the locations of execution of the EEF among the years of the period from 1987/88 to 1994/95 in whose data they are based. They can also have resulted, partly, of flaws of the experimental techniques adopted in that period of execution of EEF, particularly of the accentuated variations of the sowing date and of the stand by plot.
No Brasil, o feijão (Phaseolus vulgaris L.) é cultivado em uma gama de ambientes ecologicamente diferenciados. Por ser uma cultura altamente influenciada pela variação de ambiente, sua produtividade média no país é instável e baixa. Uma origem da oscilação da produtividade é a interação genótipo x ambiente, a qual tem sido um dos maiores entraves para a obtenção de genótipos que mantenham rendimentos consistentemente elevados nos diversos ambientes de cultivo. Os métodos propostos para a exploração da interação genótipo x ambiente são direcionados para a estabilidade do rendimento dos genótipos ou para a regionalização dos locais de cultivo. A maioria dos genótipos de feijão registrados para cultivo no Rio Grande do Sul evidencia instabilidade de rendimento. A presente pesquisa explorou dados do Ensaio Estadual de Feijão (EEF) do Rio Grande do Sul, conduzido em 24 locais no período de 1987/88 a 1994/95, com variação considerável de genótipos e de locais entre esses anos. Essa pesquisa teve dois objetivos principais: (1) avaliar a magnitude e a natureza da interação genótipo x ambiente e (2) identificar possível estratificação da região de cultivo do feijão no Estado em sub-regiões dentro das quais os genótipos tenham desempenho relativo estável. As inferências sobre os componentes da interação genótipo x ambiente foram procedidas pela análise conjunta de cada um dos oito anos e as análises de dois subconjuntos de quatro anos e do conjunto dos oito anos. Em razão de se pretender lograr uma regionalização de longo prazo, geral para os locais de cultivo do Rio Grande do Sul e para qualquer coleção de genótipos de feijão, os fatores ano, local e genótipo foram considerados aleatórios. Foram utilizadas as metodologias de máxima verossimilhança e quadrados mínimos generalizados. Essa abordagem permitiu levar em conta a estrutura incompleta e não balanceada dos dados e a heterogeneidade da variância do erro experimental. Os resultados das análises anuais revelaram alta significância do componente da interação genótipo x local em todos os anos, indicando que o desempenho relativo dos genótipos se altera entre os locais. Essa interação também se revelou significativa na análise dos oito anos, mas não significativa nas análises dos dois subconjuntos de quatro anos. Nessas três análises conjuntas de anos a interação tripla genótipo x local x ano foi altamente significativa. A indicação de desempenho heterogêneo dos genótipos entre os locais e a possibilidade do padrão desse desempenho ter alguma consistência ao longo dos anos justificou a tentativa de agrupamento desses locais. Foram efetuadas análises de agrupamento para cada um dos oito anos e para o conjunto dos oito anos, pelo método de Sokal e Michener, que utiliza a distância euclidiana como medida de similaridade. A análise de agrupamento do conjunto dos oito anos constituiu sub-regiões incoerentes com as sub-regiões formadas pelas análises anuais que, por sua vez, foram inconsistentes entre si. Essa incoerência e inconsistência de agrupamentos impossibilitaram a caracterização de uma divisão do Estado para a regionalização da indicação de cultivares. Observe-se, entretanto, que essas evidências podem ter sido influenciadas pelas consideráveis alterações dos genótipos e dos locais de condução do EEF no período de 1987/88 a 1994/95 em cujos dados elas se baseiam. Também podem ter decorrido, em parte, de falhas das técnicas experimentais adotadas nesse período de execução do EEF, particularmente das acentuadas variações da data de semeadura e do estande por parcela.
Sartorio, Simone Daniela. "Aplicações de técnicas de análise multivariada em experimentos agropecuários usando o software R". Universidade de São Paulo, 2008. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-06082008-172655/.
Texto completoThe use of the techniques of multivariate analysis is restricted to large centers of research, the higher companies and the academic environment. These techniques are very inte- resting because of the use of all answers variables simultaneously in theoretical interpretation of the data set, considering the correlations between them. One of the main obstacle to the usage of these techniques is that researchers interested in the quantitative research do not know them. The other di±culty is that most of the software that allow this type of analysis (SAS, MINITAB, BMDP, STATISTICA, S-PLUS, SYSTAT etc.) are not in public domain. Publishing the use of Multivariate techniques can improve the quality of the research, decrease the time spend and the cost, and make easy the interpretation of the structures of the data without cause damage of the information. In this report, were con¯rmed some advantages of the multivariate techniques in a univariate analysis for data of agricultural experiments. The analysis were taken with R software, a open software, \"friendly\" and free, with many statistical resources available.
Dimitrakopoulou, Vasiliki. "Bayesian variable selection in cluster analysis". Thesis, University of Kent, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.594195.
Texto completoRastelli, Riccardo y Nial Friel. "Optimal Bayesian estimators for latent variable cluster models". Springer Nature, 2018. http://dx.doi.org/10.1007/s11222-017-9786-y.
Texto completoLibros sobre el tema "CLUSTER VARIANCE"
Ritter, Gunter. Robust Cluster Analysis and Variable Selection. Taylor & Francis Group, 2015.
Buscar texto completoRitter, Gunter. Robust Cluster Analysis and Variable Selection. Taylor & Francis Group, 2014.
Buscar texto completoRitter, Gunter. Robust Cluster Analysis and Variable Selection. Taylor & Francis Group, 2014.
Buscar texto completoWelty, Daniel E. A search for giant and asymptotic-giant-branch variable stars in six globular clusters. 1986.
Buscar texto completoEiran, Ehud. Post-Colonial Settlement Strategy. Edinburgh University Press, 2019. http://dx.doi.org/10.3366/edinburgh/9781474437578.001.0001.
Texto completoMacGregor, Alex, Ana Valdes y Frances M. K. Williams. Genetics of osteoarthritis. Oxford University Press, 2013. http://dx.doi.org/10.1093/med/9780199642489.003.0044.
Texto completoCoates, David. The effects of clusters on the utility of factor analysis: The implications of systematic interrater variance for the analysis of students' evaluations of teaching. Loughborough University Business School, 1997.
Buscar texto completoFernie, J. D. Variable Stars in Globular Clusters and in Related Systems: Proceedings of the IAU Colloquium No. 21 Held at the University of Toronto, Toronto. Springer, 2011.
Buscar texto completoEuro-Asian Astronomical Society, _., ed. Astronomical and Astrophysical Transactions, Vol. 32, No. 2. Cambridge Scientific Publishers, 2021. http://dx.doi.org/10.17184/eac.9781908106797.
Texto completoFernie, J. D. Variable Stars in Globular Clusters and in Related Systems: Proceedings of the IAU Colloquium No. 21 Held at the University of Toronto, Toronto, Canada August 29-31 1972. Springer, 2012.
Buscar texto completoCapítulos de libros sobre el tema "CLUSTER VARIANCE"
Rzaḑca, Krzysztof y Francesc J. Ferri. "Incrementally Assessing Cluster Tendencies with a~Maximum Variance Cluster Algorithm". En Pattern Recognition and Image Analysis, 868–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-44871-6_100.
Texto completoWang, Shuihua, Xingxing Zhou, Guangshuai Zhang, Genlin Ji, Jiquan Yang, Zheng Zhang, Zeyuan Lu y Yudong Zhang. "Cluster Analysis by Variance Ratio Criterion and Quantum-Behaved PSO". En Cloud Computing and Security, 285–93. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27051-7_24.
Texto completoFan, Jiu-Lun, Xue-Feng Zhang y Feng Zhao. "Three-Dimension Maximum Between-Cluster Variance Image Segmentation Method Based on Chaotic Optimization". En Interactive Technologies and Sociotechnical Systems, 164–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11890881_19.
Texto completoLord, Jenny y Kevin Morgan. "Clusterin". En Genetic Variants in Alzheimer's Disease, 25–51. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7309-1_3.
Texto completoStevenson, David. "Variable Stars". En The Complex Lives of Star Clusters, 57–99. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14234-0_3.
Texto completoBrown, Kristelle, James Turton y Kevin Morgan. "Membrane-Spanning 4-Domains Subfamily A, MS4A Cluster". En Genetic Variants in Alzheimer's Disease, 159–79. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7309-1_8.
Texto completoToso, Rodrigo F., Evgeny V. Bauman, Casimir A. Kulikowski y Ilya B. Muchnik. "Experiments with a Non-convex Variance-Based Clustering Criterion". En Clusters, Orders, and Trees: Methods and Applications, 51–62. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-0742-7_3.
Texto completoCsirik, János, Leah Epstein, Csanád Imreh y Asaf Levin. "Online Clustering with Variable Sized Clusters". En Mathematical Foundations of Computer Science 2010, 282–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15155-2_26.
Texto completoCain, Nicole M., Emily B. Ansell y Anthony Pinto. "Cluster C Personality Disorders and Anxiety Disorders". En Handbook of Treating Variants and Complications in Anxiety Disorders, 349–62. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6458-7_22.
Texto completoDivya Saini, Manoj Singh y Iti Sharma. "Variance-Based Clustering for Balanced Clusters in Growing Datasets". En Proceedings of the International Congress on Information and Communication Technology, 559–65. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0767-5_58.
Texto completoActas de conferencias sobre el tema "CLUSTER VARIANCE"
Zhang, A., M. Chung, B. Lee, R. Cho, S. Kazadi y R. Vishwanath. "Variance in converging puck cluster sizes". En the first international joint conference. New York, New York, USA: ACM Press, 2002. http://dx.doi.org/10.1145/544741.544791.
Texto completoYang, Shuhong y Tong Zhang. "Images thresholding via within-cluster weighting variance". En EITCE 2021: 2021 5th International Conference on Electronic Information Technology and Computer Engineering. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3501409.3501495.
Texto completoDey, Sayak, Swagatam Das y Rammohan Mallipeddi. "The Sparse MinMax k-Means Algorithm for High-Dimensional Clustering". En Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/291.
Texto completoCandogan, Ozan, Chen Chen y Rad Niazadeh. "Correlated Cluster-Based Randomized Experiments: Robust Variance Minimization". En EC '23: 24th ACM Conference on Economics and Computation. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3580507.3597820.
Texto completoYuan, Kun, Bicheng Ying y Ali H. Sayed. "COVER: A Cluster-based Variance Reduced Method for Online Learning". En ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. http://dx.doi.org/10.1109/icassp.2019.8682527.
Texto completoGullo, Francesco, Giovanni Ponti y Andrea Tagarelli. "Minimizing the Variance of Cluster Mixture Models for Clustering Uncertain Objects". En 2010 IEEE 10th International Conference on Data Mining (ICDM). IEEE, 2010. http://dx.doi.org/10.1109/icdm.2010.134.
Texto completoYong Liu, Sha Chen y Ying Lin. "Grain bags detection based on improved maximum between-cluster variance algorithm". En 2010 International Conference on Computer Application and System Modeling (ICCASM 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccasm.2010.5622758.
Texto completoFu, Zeng, Jianfeng He, Yan Xiang, Rui Cui y Sanli Yi. "Image segmentation based on gray-level spatial correlation maximum between-cluster variance". En 2015 International Symposium on Computers and Informatics. Paris, France: Atlantis Press, 2015. http://dx.doi.org/10.2991/isci-15.2015.28.
Texto completoABDULLAH, Mohammed, Rizgar AHMED y Yener ALTUN. "Comparison Between Factor Analysis and Cluster Analysis to Determine the Most Important Affecting Factors for Students' Admission and Their Interests in The Specializations: A Sample of Salahaddin University-Erbil". En 3rd International Conference of Mathematics and its Applications. Salahaddin University-Erbil, 2020. http://dx.doi.org/10.31972/ticma22.03.
Texto completoNa, Wang. "The application of the improved maximum between-cluster variance method in special images". En ICNSER2020: The 2nd International Conference On Industrial Control Network And System Engineering Research. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3411016.3411021.
Texto completoInformes sobre el tema "CLUSTER VARIANCE"
Kryzhanivs'kyi, Evstakhii, Liliana Horal, Iryna Perevozova, Vira Shyiko, Nataliia Mykytiuk y Maria Berlous. Fuzzy cluster analysis of indicators for assessing the potential of recreational forest use. [б. в.], octubre de 2020. http://dx.doi.org/10.31812/123456789/4470.
Texto completoKott, Phillip S. The Degrees of Freedom of a Variance Estimator in a Probability Sample. RTI Press, agosto de 2020. http://dx.doi.org/10.3768/rtipress.2020.mr.0043.2008.
Texto completoPetre, Melinda. Comparison of Outputs for Variable Combinations Used in Cluster Analysis on Polarmetric Imagery. Fort Belvoir, VA: Defense Technical Information Center, enero de 2008. http://dx.doi.org/10.21236/ada476766.
Texto completoThompson, William L. Comparison of Three Plot Selection Methods for Estimating Change in Temporally Variable, Spatially Clustered Populations. Office of Scientific and Technical Information (OSTI), julio de 2001. http://dx.doi.org/10.2172/785591.
Texto completoLevisohn, Sharon, Maricarmen Garcia, David Yogev y Stanley Kleven. Targeted Molecular Typing of Pathogenic Avian Mycoplasmas. United States Department of Agriculture, enero de 2006. http://dx.doi.org/10.32747/2006.7695853.bard.
Texto completoOr, Dani, Shmulik Friedman y Jeanette Norton. Physical processes affecting microbial habitats and activity in unsaturated agricultural soils. United States Department of Agriculture, octubre de 2002. http://dx.doi.org/10.32747/2002.7587239.bard.
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