Information-incorporated clustering analysis of disease prevalence trends-Reference-Cited by-同舟云学术

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Information-incorporated clustering analysis of disease prevalence trends

Published:2024-06-01 Issue:2 Volume:18 Page:
ISSN:1932-6157
Container-title:The Annals of Applied Statistics
language:
Short-container-title:Ann. Appl. Stat.

Author:

Ma Chenjin¹,Lin Cunjie²,Xue Yuan³,Zhang Sanguo³,Zhang Qingzhao⁴,Ma Shuangge⁵

Affiliation:

1. Department of Statistics and Data Science, Beijing University of Technology

2. School of Statistics, Renmin University of China

3. School of Mathematics Sciences, University of Chinese Academy of Sciences

4. Department of Statistics, School of Economics, Xiamen University

5. Department of Biostatistics, Yale School of Public Health

Publisher

Institute of Mathematical Statistics

Reference32 articles.

1. SCHUMAKER, L. L. (2007). Spline Functions: Basic Theory, 3rd ed. Cambridge Mathematical Library. Cambridge Univ. Press, Cambridge.

2. JIANG, Y., HE, Y. and ZHANG, H. (2016). Variable selection with prior information for generalized linear models via the prior LASSO method. J. Amer. Statist. Assoc. 111 355–376.

3. Jacques, J. and Preda, C. (2014). Functional data clustering: A survey. Adv. Data Anal. Classif. 8 231–255.

4. Ma, S. and Huang, J. (2017). A concave pairwise fusion approach to subgroup analysis. J. Amer. Statist. Assoc. 112 410–423.

5. Zhou, X., Lei, L., Liu, J., Halu, A., Zhang, Y., Li, B., Guo, Z., Liu, G., Sun, C. et al. (2018). A systems approach to refine disease taxonomy by integrating phenotypic and molecular networks. EBioMedicine 31 79–91.

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