## The Annals of Probability

- Ann. Probab.
- Volume 18, Number 1 (1990), 160-189.

### Functional Laws of the Iterated Logarithm for the Product-Limit Estimator of a Distribution Function Under Random Censorship or Truncation

#### Abstract

Functional laws of the iterated logarithm are established for a modified version of the classical product-limit estimator of a distribution function when the data are subject to random censorship or truncation. These functional laws are shown to hold for the entire interval $I$ over which the distribution function can be consistently estimated, under basically the same assumptions that have been used in the literature to establish the weak convergence of the normalized estimator in $D(I)$. Making use of stochastic integral representations and empirical process theory, strong approximations involving i.i.d. continuous-parameter martingales are developed for the product-limit estimator, and these strong approximations are then applied to derive the functional laws of the iterated logarithm.

#### Article information

**Source**

Ann. Probab. Volume 18, Number 1 (1990), 160-189.

**Dates**

First available: 19 April 2007

**Permanent link to this document**

http://projecteuclid.org/euclid.aop/1176990943

**JSTOR**

links.jstor.org

**Digital Object Identifier**

doi:10.1214/aop/1176990943

**Mathematical Reviews number (MathSciNet)**

MR1043942

**Zentralblatt MATH identifier**

0705.62040

**Subjects**

Primary: 60F15: Strong theorems

Secondary: 60F17: Functional limit theorems; invariance principles 60H05: Stochastic integrals 60B12: Limit theorems for vector-valued random variables (infinite- dimensional case) 62G05: Estimation

**Keywords**

Functional LIL strong approximation martingales stochastic integrals empirical processes product-limit estimator reproducing kernel Hilbert space

#### Citation

Gu, Ming Gao; Lai, Tze Leung. Functional Laws of the Iterated Logarithm for the Product-Limit Estimator of a Distribution Function Under Random Censorship or Truncation. The Annals of Probability 18 (1990), no. 1, 160--189. doi:10.1214/aop/1176990943. http://projecteuclid.org/euclid.aop/1176990943.