Statistical Diagnosis for Random Right Censored Data Based on Kaplan-Meier Product Limit Estimate
Affiliation(s)
Department of Fundamental Course, Air Force Logistics College, Xuzhou, China. School of Statistics and Applied Mathematics, Anhui University of Finance and Economics, Bengbu, China.
Department of Fundamental Course, Air Force Logistics College, Xuzhou, China. School of Statistics and Applied Mathematics, Anhui University of Finance and Economics, Bengbu, China.
ABSTRACT
In this work, we consider statistical diagnostic for random right censored data based on K-M product limit estimator. Under the definition of K-M product limit estimator, we obtain that the relation formula between estimators. Similar to complete data, we define likelihood displacement and likelihood ratio statistic. Through a real data application, we show that our proposed procedure is validity.
KEYWORDS
Random Right Censorship, Kaplan-Meier Product-Limit Estimator, Empirical Likelihood, Outliers, Influence Analysis
Random Right Censorship, Kaplan-Meier Product-Limit Estimator, Empirical Likelihood, Outliers, Influence Analysis
Cite this paper
Wang, S. , Deng, X. and Zheng, L. (2014) Statistical Diagnosis for Random Right Censored Data Based on Kaplan-Meier Product Limit Estimate. Open Journal of Statistics, 4, 313-317. doi: 10.4236/ojs.2014.44031.
Wang, S. , Deng, X. and Zheng, L. (2014) Statistical Diagnosis for Random Right Censored Data Based on Kaplan-Meier Product Limit Estimate. Open Journal of Statistics, 4, 313-317. doi: 10.4236/ojs.2014.44031.
References
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http://dx.doi.org/10.4310/SII.2008.v1.n1.a15 [5] Owen, A. (2001) Empirical Likelihood. Chapman and Hall, New York.
http://dx.doi.org/10.1201/9781420036152 [6] Andersen, P.K., Borgan, O., Gill, R.D. and Keiding, N. (1993) Statistical Models Based on Counting Processes. Springer-Verlag, New York. [7] Wang, Q.H. (2006) Analysis of Survival Data. Science Press, Beijing. [8] Wang, S.L., Feng, Y. and Liu, X.B. (2010) Statistical Diagnostics of Nonparametric Regression Model with Random Right Censorship. Journal of Hefei University of Technology (Natural Science), 33, 470-473.
[1] Cook, R.D. and Weisberg, S. (1982) Residuals and Influence in Regression. Chapman and Hall, New York. [2] Wei, B.C., Lu, G.B. and Shi, J.Q. (1990) Statistical Diagnostics. Publishing House of Southeast University, Nanjing. [3] Cai, Z., Fan, J. and Li, R. (2000) Efficient Estimation and Inferences for Varying-Coefficient Models. Journal of American Statistical Association, 95, 888-902.
http://dx.doi.org/10.1080/01621459.2000.10474280 [4] Fan, J. and Zhang, W. (2008) Statistical Methods with Varying Coefficient Models. Statistics and Its Interface, 1, 179-195.
http://dx.doi.org/10.4310/SII.2008.v1.n1.a15 [5] Owen, A. (2001) Empirical Likelihood. Chapman and Hall, New York.
http://dx.doi.org/10.1201/9781420036152 [6] Andersen, P.K., Borgan, O., Gill, R.D. and Keiding, N. (1993) Statistical Models Based on Counting Processes. Springer-Verlag, New York. [7] Wang, Q.H. (2006) Analysis of Survival Data. Science Press, Beijing. [8] Wang, S.L., Feng, Y. and Liu, X.B. (2010) Statistical Diagnostics of Nonparametric Regression Model with Random Right Censorship. Journal of Hefei University of Technology (Natural Science), 33, 470-473.