Identifying Outlying and Influential Clusters in Multivariate Survival Data Models

Author:

Kaombe Tsirizani M.,Manda Samuel O. M.

Publisher

Springer International Publishing

Reference39 articles.

1. Abrahantes, J., & Burzykowski, T. (2005). A version of the EM algorithm for proportional hazard model with random effects. Biometrical Journal, 47(6), 847–862.

2. Aguinis, H., Gottfredson, R., & Joo, H. (2013). Best-practice recommendations for defining, identifying, and handling outliers. Organizational Research Methods, 16(2), 270–301.

3. Belsley, D. A., Kuh, E., & Welsch, R. E. (2005). Regression diagnostics: Identifying influential data and sources of collinearity, vol. 571. New York: John Wiley & Sons.

4. Brilleman, S., Rory, W., Moreno-Betancur, M., & Crowther, M. (2018). simsurv: A package for simulating simple or complex survival data. In UseR! Conference 2018, Brisbane, Australia. Monash University.

5. Cain, K., & Lange, N. (1984). Approximate case influence for the proportional hazards regression model with censored data. Biometrics, 40(2), 493–499.

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