Browsing by Subject "empirical likelihood"

Now showing 1 - 3 of 3
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    Asymptotic normality of quadratic forms with random vectors of increasing dimension
    (Elsevier, 2018-03) Peng, Hanxiang; Schick, Anton; Mathematical Sciences, School of Science
    This paper provides sufficient conditions for the asymptotic normality of quadratic forms of averages of random vectors of increasing dimension and improves on conditions found in the literature. Such results are needed in applications of Owen’s empirical likelihood when the number of constraints is allowed to grow with the sample size. Indeed, the results of this paper are already used in Peng and Schick (2013) for this purpose. We also demonstrate how our results can be used to obtain the asymptotic distribution of the empirical likelihood with an increasing number of constraints under contiguous alternatives. In addition, we discuss potential applications of our result. The first example focuses on a chi-square test with an increasing number of cells. The second example treats testing for the equality of the marginal distributions of a bivariate random vector. The third example generalizes a result of Schott (2005) by showing that a standardized version of his test for diagonality of the dispersion matrix of a normal random vector is asymptotically standard normal even if the dimension increases faster than the sample size. Schott’s result requires the dimension and the sample size to be of the same order.
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    Empirical Likelihood Ratio Tests for Coe cients in High Dimensional Heteroscedastic Linear Models
    (ICSA, 2018) Wang, Honglang; Zhong, Ping-Shou; Cui, Yuehua; Mathematical Sciences, School of Science
    This paper considers hypothesis testing problems for a low-dimensional coefficient vector in a high-dimensional linear model with heteroscedastic variance. Heteroscedasticity is a commonly observed phenomenon in many applications, including finance and genomic studies. Several statistical inference procedures have been proposed for low-dimensional coefficients in a high-dimensional linear model with homoscedastic variance, which are not applicable for models with heteroscedastic variance. The heterscedasticity issue has been rarely investigated and studied. We propose a simple inference procedure based on empirical likelihood to overcome the heteroscedasticity issue. The proposed method is able to make valid inference even when the conditional variance of random error is an unknown function of high-dimensional predictors. We apply our inference procedure to three recently proposed estimating equations and establish the asymptotic distributions of the proposed methods. Simulation studies and real data applications are conducted to demonstrate the proposed methods.
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    Unified empirical likelihood ratio tests for functional concurrent linear models and the phase transition from sparse to dense functional data
    (Wiley, 2018-03) Wang, Honglang; Zhong, Ping-Shou; Cui, Yuehua; Li, Yehua; Mathematical Sciences, School of Science
    We consider the problem of testing functional constraints in a class of functional concurrent linear models where both the predictors and the response are functional data measured at discrete time points. We propose test procedures based on the empirical likelihood with bias‐corrected estimating equations to conduct both pointwise and simultaneous inferences. The asymptotic distributions of the test statistics are derived under the null and local alternative hypotheses, where sparse and dense functional data are considered in a unified framework. We find a phase transition in the asymptotic null distributions and the orders of detectable alternatives from sparse to dense functional data. Specifically, the tests proposed can detect alternatives of √n‐order when the number of repeated measurements per curve is of an order larger than urn:x-wiley:13697412:media:rssb12246:rssb12246-math-0001 with n being the number of curves. The transition points urn:x-wiley:13697412:media:rssb12246:rssb12246-math-0002 for pointwise and simultaneous tests are different and both are smaller than the transition point in the estimation problem. Simulation studies and real data analyses are conducted to demonstrate the methods proposed.