A
penalized empirical likelihood method in high dimensions
Soumedra Nath Lahiri (Texas A & M University)
16/03/11
We formulate a penalized empirical likelihood (PEL) method for
inference on the population mean when the dimension of the observations
become unbounded with the sample size. We derive the asymptotic
distribution of the PEL ratio statistic. We show that the limit
distribution of the proposed PEL ratio statistic can vary widely
depending on the correlation structure of the components of the
observations that we classify as (i) non-Ergodic, (ii) long range
dependent, and (iii) short range dependent. The limit laws differ from
the usual chi-squared limit of the empirical likelihood ratio statistic
in the finite dimensional case. We propose a subsampling approximation
for calibrating the PEL ratio test statistic and establish its
validity. Finite sample properties of the method are investigated
through a simulation study.
Joint work with Deep
Mukhopahyay.
Back to the seminar
homepage
LSE Home Page |
Departmental
Home Page
[Last modified: Jun. 9th 2011 by Kostas
Kalogeropoulos]