Littérature scientifique sur le sujet « CLUSTER VARIANCE »
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Articles de revues sur le sujet "CLUSTER VARIANCE"
Gregg, Mary, Somnath Datta et Doug Lorenz. « Variance estimation in tests of clustered categorical data with informative cluster size ». Statistical Methods in Medical Research 29, no 11 (8 juin 2020) : 3396–408. http://dx.doi.org/10.1177/0962280220928572.
Texte intégralSankaran, M., M. R. Dinesh, D. C. S. Gowda et R. Venugopalan. « Genetic analysis in mango (Mangifera indica L.) based on fruit characteristics of 400 genotypes ». Journal of Horticultural Sciences 15, no 2 (31 décembre 2020) : 161–72. http://dx.doi.org/10.24154/jhs.2020.v15i02.007.
Texte intégralSankaran, M., M. R. Dinesh, D. C. S. Gowda et R. Venugopalan. « Genetic analysis in mango (Mangifera indica L.) based on fruit characteristics of 400 genotypes ». Journal of Horticultural Sciences 15, no 2 (31 décembre 2020) : 161–72. http://dx.doi.org/10.24154/jhs.v15i2.944.
Texte intégralYasmeen, Uzma, Muhammad Noor-ul-Amin et Muhammad Hanif. « Variance estimation in stratified adaptive cluster sampling ». Statistics in Transition New Series 23, no 1 (1 mars 2022) : 173–84. http://dx.doi.org/10.2478/stattrans-2022-0010.
Texte intégralVeenman, C. J., M. J. T. Reinders et E. Backer. « A maximum variance cluster algorithm ». IEEE Transactions on Pattern Analysis and Machine Intelligence 24, no 9 (septembre 2002) : 1273–80. http://dx.doi.org/10.1109/tpami.2002.1033218.
Texte intégralA.K, et al., Shukla. « Variance Function in Cluster Sampling ». International Journal of Computational and Theoretical Statistics 2, no 1 (1 mai 2015) : 25–30. http://dx.doi.org/10.12785/ijcts/020103.
Texte intégralTerry, L. Irene, et Gloria DeGrandi-Hoffman. « MONITORING WESTERN FLOWER THRIPS (THYSANOPTERA : THRIPIDAE) IN “GRANNY SMITH” APPLE BLOSSOM CLUSTERS ». Canadian Entomologist 120, no 11 (novembre 1988) : 1003–16. http://dx.doi.org/10.4039/ent1201003-11.
Texte intégralVostrá Vydrová, Hana, et Zuzana Novotná. « Evaluation of disparities in living standards of regions of the Czech Republic ». Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 60, no 4 (2012) : 407–14. http://dx.doi.org/10.11118/actaun201260040407.
Texte intégralScott, JoAnna M., Allan deCamp, Michal Juraska, Michael P. Fay et 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, no 2 (29 septembre 2014) : 583–97. http://dx.doi.org/10.1177/0962280214552092.
Texte intégralGrilli, Leonardo, et Carla Rampichini. « The Role of Sample Cluster Means in Multilevel Models ». Methodology 7, no 4 (1 août 2011) : 121–33. http://dx.doi.org/10.1027/1614-2241/a000030.
Texte intégralThèses sur le sujet "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.
Texte intégralYou, 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.
Texte intégralPark, 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.
Texte intégralDunning, 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.
Texte intégralRandriatsiferana, 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.
Texte intégralTo 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.
Texte intégralMaster 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.
Texte intégralIn 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/.
Texte intégralThe 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.
Texte intégralRastelli, Riccardo, et Nial Friel. « Optimal Bayesian estimators for latent variable cluster models ». Springer Nature, 2018. http://dx.doi.org/10.1007/s11222-017-9786-y.
Texte intégralLivres sur le sujet "CLUSTER VARIANCE"
Ritter, Gunter. Robust Cluster Analysis and Variable Selection. Taylor & Francis Group, 2015.
Trouver le texte intégralRitter, Gunter. Robust Cluster Analysis and Variable Selection. Taylor & Francis Group, 2014.
Trouver le texte intégralRitter, Gunter. Robust Cluster Analysis and Variable Selection. Taylor & Francis Group, 2014.
Trouver le texte intégralWelty, Daniel E. A search for giant and asymptotic-giant-branch variable stars in six globular clusters. 1986.
Trouver le texte intégralEiran, Ehud. Post-Colonial Settlement Strategy. Edinburgh University Press, 2019. http://dx.doi.org/10.3366/edinburgh/9781474437578.001.0001.
Texte intégralMacGregor, Alex, Ana Valdes et Frances M. K. Williams. Genetics of osteoarthritis. Oxford University Press, 2013. http://dx.doi.org/10.1093/med/9780199642489.003.0044.
Texte intégralCoates, 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.
Trouver le texte intégralFernie, 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.
Trouver le texte intégralEuro-Asian Astronomical Society, _., dir. Astronomical and Astrophysical Transactions, Vol. 32, No. 2. Cambridge Scientific Publishers, 2021. http://dx.doi.org/10.17184/eac.9781908106797.
Texte intégralFernie, 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.
Trouver le texte intégralChapitres de livres sur le sujet "CLUSTER VARIANCE"
Rzaḑca, Krzysztof, et Francesc J. Ferri. « Incrementally Assessing Cluster Tendencies with a~Maximum Variance Cluster Algorithm ». Dans 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.
Texte intégralWang, Shuihua, Xingxing Zhou, Guangshuai Zhang, Genlin Ji, Jiquan Yang, Zheng Zhang, Zeyuan Lu et Yudong Zhang. « Cluster Analysis by Variance Ratio Criterion and Quantum-Behaved PSO ». Dans Cloud Computing and Security, 285–93. Cham : Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27051-7_24.
Texte intégralFan, Jiu-Lun, Xue-Feng Zhang et Feng Zhao. « Three-Dimension Maximum Between-Cluster Variance Image Segmentation Method Based on Chaotic Optimization ». Dans Interactive Technologies and Sociotechnical Systems, 164–73. Berlin, Heidelberg : Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11890881_19.
Texte intégralLord, Jenny, et Kevin Morgan. « Clusterin ». Dans 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.
Texte intégralStevenson, David. « Variable Stars ». Dans 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.
Texte intégralBrown, Kristelle, James Turton et Kevin Morgan. « Membrane-Spanning 4-Domains Subfamily A, MS4A Cluster ». Dans 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.
Texte intégralToso, Rodrigo F., Evgeny V. Bauman, Casimir A. Kulikowski et Ilya B. Muchnik. « Experiments with a Non-convex Variance-Based Clustering Criterion ». Dans 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.
Texte intégralCsirik, János, Leah Epstein, Csanád Imreh et Asaf Levin. « Online Clustering with Variable Sized Clusters ». Dans 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.
Texte intégralCain, Nicole M., Emily B. Ansell et Anthony Pinto. « Cluster C Personality Disorders and Anxiety Disorders ». Dans 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.
Texte intégralDivya Saini, Manoj Singh et Iti Sharma. « Variance-Based Clustering for Balanced Clusters in Growing Datasets ». Dans 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.
Texte intégralActes de conférences sur le sujet "CLUSTER VARIANCE"
Zhang, A., M. Chung, B. Lee, R. Cho, S. Kazadi et R. Vishwanath. « Variance in converging puck cluster sizes ». Dans the first international joint conference. New York, New York, USA : ACM Press, 2002. http://dx.doi.org/10.1145/544741.544791.
Texte intégralYang, Shuhong, et Tong Zhang. « Images thresholding via within-cluster weighting variance ». Dans 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.
Texte intégralDey, Sayak, Swagatam Das et Rammohan Mallipeddi. « The Sparse MinMax k-Means Algorithm for High-Dimensional Clustering ». Dans 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.
Texte intégralCandogan, Ozan, Chen Chen et Rad Niazadeh. « Correlated Cluster-Based Randomized Experiments : Robust Variance Minimization ». Dans EC '23 : 24th ACM Conference on Economics and Computation. New York, NY, USA : ACM, 2023. http://dx.doi.org/10.1145/3580507.3597820.
Texte intégralYuan, Kun, Bicheng Ying et Ali H. Sayed. « COVER : A Cluster-based Variance Reduced Method for Online Learning ». Dans ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. http://dx.doi.org/10.1109/icassp.2019.8682527.
Texte intégralGullo, Francesco, Giovanni Ponti et Andrea Tagarelli. « Minimizing the Variance of Cluster Mixture Models for Clustering Uncertain Objects ». Dans 2010 IEEE 10th International Conference on Data Mining (ICDM). IEEE, 2010. http://dx.doi.org/10.1109/icdm.2010.134.
Texte intégralYong Liu, Sha Chen et Ying Lin. « Grain bags detection based on improved maximum between-cluster variance algorithm ». Dans 2010 International Conference on Computer Application and System Modeling (ICCASM 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccasm.2010.5622758.
Texte intégralFu, Zeng, Jianfeng He, Yan Xiang, Rui Cui et Sanli Yi. « Image segmentation based on gray-level spatial correlation maximum between-cluster variance ». Dans 2015 International Symposium on Computers and Informatics. Paris, France : Atlantis Press, 2015. http://dx.doi.org/10.2991/isci-15.2015.28.
Texte intégralABDULLAH, Mohammed, Rizgar AHMED et 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 ». Dans 3rd International Conference of Mathematics and its Applications. Salahaddin University-Erbil, 2020. http://dx.doi.org/10.31972/ticma22.03.
Texte intégralNa, Wang. « The application of the improved maximum between-cluster variance method in special images ». Dans 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.
Texte intégralRapports d'organisations sur le sujet "CLUSTER VARIANCE"
Kryzhanivs'kyi, Evstakhii, Liliana Horal, Iryna Perevozova, Vira Shyiko, Nataliia Mykytiuk et Maria Berlous. Fuzzy cluster analysis of indicators for assessing the potential of recreational forest use. [б. в.], octobre 2020. http://dx.doi.org/10.31812/123456789/4470.
Texte intégralKott, Phillip S. The Degrees of Freedom of a Variance Estimator in a Probability Sample. RTI Press, août 2020. http://dx.doi.org/10.3768/rtipress.2020.mr.0043.2008.
Texte intégralPetre, Melinda. Comparison of Outputs for Variable Combinations Used in Cluster Analysis on Polarmetric Imagery. Fort Belvoir, VA : Defense Technical Information Center, janvier 2008. http://dx.doi.org/10.21236/ada476766.
Texte intégralThompson, William L. Comparison of Three Plot Selection Methods for Estimating Change in Temporally Variable, Spatially Clustered Populations. Office of Scientific and Technical Information (OSTI), juillet 2001. http://dx.doi.org/10.2172/785591.
Texte intégralLevisohn, Sharon, Maricarmen Garcia, David Yogev et Stanley Kleven. Targeted Molecular Typing of Pathogenic Avian Mycoplasmas. United States Department of Agriculture, janvier 2006. http://dx.doi.org/10.32747/2006.7695853.bard.
Texte intégralOr, Dani, Shmulik Friedman et Jeanette Norton. Physical processes affecting microbial habitats and activity in unsaturated agricultural soils. United States Department of Agriculture, octobre 2002. http://dx.doi.org/10.32747/2002.7587239.bard.
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