Bernoulli
- Bernoulli
- Volume 7, Number 1 (2001), 33-61.
Sample splitting with Markov chains
Abstract
Sample splitting techniques play an important role in constructing estimates with prescribed influence functions in semi-parametric and nonparametric models when the observations are independent and identically distributed. This paper shows how a contiguity result can be used to modify these techniques to the case when the observations come from a stationary and ergodic Markov chain. As a consequence, sufficient conditions for the construction of efficient estimates in semi-parametric Markov chain models are obtained. The applicability of the resulting theory is demonstrated by constructing an estimate of the innovation variance in a nonparametric autoregression model, by constructing a weighted least-squares estimate with estimated weights in an autoregressive model with martingale innovations, and by constructing an efficient and adaptive estimate of the autoregression parameter in a heteroscedastic autoregressive model with symmetric errors.
Article information
Source
Bernoulli, Volume 7, Number 1 (2001), 33-61.
Dates
First available in Project Euclid: 29 March 2004
Permanent link to this document
https://projecteuclid.org/euclid.bj/1080572338
Mathematical Reviews number (MathSciNet)
MR1811743
Zentralblatt MATH identifier
0997.62062
Keywords
contiguity efficient estimation ergodicity heteroscedastic autoregressive model nonparametric autoregressive model semi-parametric models stationary Markov chains V-uniform ergodicity weighted least-squares estimation
Citation
Schick, Anton. Sample splitting with Markov chains. Bernoulli 7 (2001), no. 1, 33--61. https://projecteuclid.org/euclid.bj/1080572338
