Empirical likelihood test for high-dimensional two-sample model
نویسندگان
چکیده
منابع مشابه
Jackknife Empirical Likelihood Test for Equality of Two High Dimensional Means
There is a long history of testing the equality of two multivariate means. A popular test is the Hotelling T , but in large dimensions it performs poorly due to the possible inconsistency of sample covariance estimation. Bai and Saranadasa (1996) and Chen and Qin (2010) proposed tests not involving the sample covariance, and derived asymptotic limits, which depend on whether the dimension is fi...
متن کاملEmpirical Likelihood for High Dimensional Data
Since Owen (1988, 1990) introduced the empirical likelihood method for constructing a confidence interval or region for the mean of a random variable or vector, empirical likelihood methods have been extended to many different settings. Recently, Hjort, McKeague and Van Keilegom (2004) extended the standard empirical likelihood method to the case where the data dimension depends on the sample s...
متن کاملExtending the two-sample empirical likelihood method
In this paper we establish the empirical likelihood method for the two-sample case in a general framework. We show that the result of Qin and Zhao (2000) basically covers the following two-sample models: the differences of two sample means, smooth Huber estimators, distribution and quantile functions, ROC curves, probability-probability (P-P) and quantile-quantile (Q-Q) plots. Finally, the stru...
متن کاملMultivariate two-sample extended empirical likelihood
Jing (1995) and Liu et al. (2008) studied the two-sample empirical likelihood and showed it is Bartlett correctable for the univariate and multivariate cases, respectively. We expand its domain to the full parameter space and obtain a two-sample extended empirical likelihood which is more accurate and can also achieve the second-order accuracy of the Bartlett correction. AMS 2000 subject classi...
متن کاملAn adaptive two-sample test for high-dimensional means
Several two-sample tests for high-dimensional data have been proposed recently, but they are powerful only against certain limited alternative hypotheses. In practice, since the true alternative hypothesis is unknown, it is unclear how to choose a powerful test. We propose an adaptive test that maintains high power across a wide range of situations, and study its asymptotic properties. Its fini...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Statistical Planning and Inference
سال: 2016
ISSN: 0378-3758
DOI: 10.1016/j.jspi.2016.05.002