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A Quantitative Study of Gene Regulation Involved in the Immune Response of Anopheline Mosquitoes: An Application of Bayesian Hierarchical Clustering of Curves

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  • Heard, Nicholas A.
  • Holmes, Christopher C.
  • Stephens, David A.

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Suggested Citation

  • Heard, Nicholas A. & Holmes, Christopher C. & Stephens, David A., 2006. "A Quantitative Study of Gene Regulation Involved in the Immune Response of Anopheline Mosquitoes: An Application of Bayesian Hierarchical Clustering of Curves," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 18-29, March.
    • Handle: RePEc:bes:jnlasa:v:101:y:2006:p:18-29
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    Cited by:

    1. Ando, Tomohiro & Bai, Jushan, 2021. "Large-scale generalized linear longitudinal data models with grouped patterns of unobserved heterogeneity," MPRA Paper 111431, University Library of Munich, Germany.
    2. Alessandra Guglielmi & Francesca Ieva & Anna M. Paganoni & Fabrizio Ruggeri & Jacopo Soriano, 2014. "Semiparametric Bayesian models for clustering and classification in the presence of unbalanced in-hospital survival," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 63(1), pages 25-46, January.
    3. Burghardt, Elliot & Sewell, Daniel & Cavanaugh, Joseph, 2022. "Agglomerative and divisive hierarchical Bayesian clustering," Computational Statistics & Data Analysis, Elsevier, vol. 176(C).
    4. Bruno Scarpa & David B. Dunson, 2009. "Bayesian Hierarchical Functional Data Analysis Via Contaminated Informative Priors," Biometrics, The International Biometric Society, vol. 65(3), pages 772-780, September.
    5. Gniewko Niedbała & Jarosław Kurek & Bartosz Świderski & Tomasz Wojciechowski & Izabella Antoniuk & Krzysztof Bobran, 2022. "Prediction of Blueberry ( Vaccinium corymbosum L.) Yield Based on Artificial Intelligence Methods," Agriculture, MDPI, vol. 12(12), pages 1-27, December.
    6. Amovin-Assagba, Martial & Gannaz, Irène & Jacques, Julien, 2022. "Outlier detection in multivariate functional data through a contaminated mixture model," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).
    7. Shotwell, Matthew S., 2013. "profdpm: An R Package for MAP Estimation in a Class of Conjugate Product Partition Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 53(i08).
    8. James G. Booth & George Casella & James P. Hobert, 2008. "Clustering using objective functions and stochastic search," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(1), pages 119-139, February.
    9. Angelini, Claudia & De Canditiis, Daniela & Pensky, Marianna, 2009. "Bayesian models for two-sample time-course microarray experiments," Computational Statistics & Data Analysis, Elsevier, vol. 53(5), pages 1547-1565, March.
    10. Aßmann, Christian & Boysen-Hogrefe, Jens, 2011. "A Bayesian approach to model-based clustering for binary panel probit models," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 261-279, January.
    11. Robert Darkins & Emma J Cooke & Zoubin Ghahramani & Paul D W Kirk & David L Wild & Richard S Savage, 2013. "Accelerating Bayesian Hierarchical Clustering of Time Series Data with a Randomised Algorithm," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-9, April.
    12. Jane L. Harvill & Priya Kohli & Nalini Ravishanker, 2017. "Clustering Nonlinear, Nonstationary Time Series Using BSLEX," Methodology and Computing in Applied Probability, Springer, vol. 19(3), pages 935-955, September.
    13. Freeman, G. & Smith, J.Q., 2011. "Bayesian MAP model selection of chain event graphs," Journal of Multivariate Analysis, Elsevier, vol. 102(7), pages 1152-1165, August.
    14. Julien Jacques & Cristian Preda, 2014. "Functional data clustering: a survey," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(3), pages 231-255, September.
    15. Aßmann, Christian & Boysen-Hogrefe, Jens, 2009. "A bayesian approach to model-based clustering for panel probit models," Economics Working Papers 2009-03, Christian-Albrechts-University of Kiel, Department of Economics.
    16. Sylvia Frühwirth-Schnatter, 2011. "Panel data analysis: a survey on model-based clustering of time series," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 5(4), pages 251-280, December.
    17. repec:jss:jstsof:47:i05 is not listed on IDEAS
    18. Jim Q. Smith & Paul E. Anderson & Silvia Liverani, 2008. "Separation measures and the geometry of Bayes factor selection for classification," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(5), pages 957-980, November.
    19. Ian C McDowell & Dinesh Manandhar & Christopher M Vockley & Amy K Schmid & Timothy E Reddy & Barbara E Engelhardt, 2018. "Clustering gene expression time series data using an infinite Gaussian process mixture model," PLOS Computational Biology, Public Library of Science, vol. 14(1), pages 1-27, January.
    20. Daewon Yang & Taeryon Choi & Eric Lavigne & Yeonseung Chung, 2022. "Non‐parametric Bayesian covariate‐dependent multivariate functional clustering: An application to time‐series data for multiple air pollutants," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1521-1542, November.

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