Browsing by Author "Schick, Anton"

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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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    Inference about the slope in linear regression: an empirical likelihood approach
    (Springer, 2017) Müller, Ursula U.; Peng, Hanxiang; Schick, Anton; Mathematical Sciences, School of Science
    We present a new, efficient maximum empirical likelihood estimator for the slope in linear regression with independent errors and covariates. The estimator does not require estimation of the influence function, in contrast to other approaches, and is easy to obtain numerically. Our approach can also be used in the model with responses missing at random, for which we recommend a complete case analysis. This suffices thanks to results by Müller and Schick (Bernoulli 23:2693–2719, 2017), which demonstrate that efficiency is preserved. We provide confidence intervals and tests for the slope, based on the limiting Chi-square distribution of the empirical likelihood, and a uniform expansion for the empirical likelihood ratio. The article concludes with a small simulation study.
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    Maximum empirical likelihood estimation and related topics
    (IMS, 2018) Peng, Hanxiang; Schick, Anton; Mathematical Sciences, School of Science
    This article develops a theory of maximum empirical likelihood estimation and empirical likelihood ratio testing with irregular and estimated constraint functions that parallels the theory for parametric models and is tailored for semiparametric models. The key is a uniform local asymptotic normality condition for the local empirical likelihood ratio. This condition is shown to hold under mild assumptions on the constraint function. Applications of our results are discussed to inference problems about quantiles under possibly additional information on the underlying distribution and to residual-based inference about quantiles.