An Empirical Central Limit Theorem for the Kaplan-Meier Integral Process on [0,$\infty$)

  • Bae, Jong-Sig (Department of Mathematics, Sung Kyun Kwan University, Suwon, 440-746)
  • Published : 1997.06.01

Abstract

In this paper we investigate weak convergence of the intergral processes whose index set is the non-compact infinite time interval. Our first goal is to develop the empirical central limit theorem as random elements of [0, .infty.) for an integral process which is constructed from iid variables. In developing the weak convergence as random elements of D[0, .infty.), we will use a result of Ossiander(4) whose proof heavily depends on the total boundedness of the index set. Our next goal is to establish the empirical central limit theorem for the Kaplan-Meier integral process as random elements of D[0, .infty.). In achieving the the goal, we will use the above iid result, a representation of State(6) on the Kaplan-Meier integral, and a lemma on the uniform order of convergence. The first result, in some sense, generalizes the result of empirical central limit therem of Pollard(5) where the process is regarded as random elements of D[-.infty., .infty.] and the sample paths of limiting Gaussian process may jump. The second result generalizes the first result to random censorship model. The later also generalizes one dimensional central limit theorem of Stute(6) to a process version. These results may be used in the nonparametric statistical inference.

Keywords

References

  1. Probability. Reading Breiman, L.
  2. Lecture notes in Math Lectures on Probability Theory Gill, R. D.
  3. Journal of American Statistical Association v.53 Nonparametric estimation from incomplete observations Kaplan, E. L.;Meier, P.
  4. Annal of Probability v.15 A Central Limit Theorem under Metric Entropy with L₂Bracketing Ossiander, M.
  5. Convergence of Stochastic Processes Pollard, D.
  6. Annal of Statististics v.23 The Central Limit Theorem under Random Censorship Stute, W.