BACKGROUND/AIMS There is a growing interest regarding the effect of differential misclassification on power and type I error rate in genome-wide association studies. We present an extension of a previously published test statistic: the likelihood ratio test allowing for errors (LRTAE). This test uses double-sample information on a subset of individuals to increase power for genetic association ...

• In this paper the authors introduce the hyper-block matrix sphericity test which is a generalization of both the block-matrix and the block-scalar sphericity tests and as such also of the common sphericity test. This test is a tool of crucial importance to verify elaborate assumptions on covariance matrix structures, namely on metaanalysis and error covariance structures in mixed models and m...

In this work near-exact distributions for the likelihood ratio test (l.r.t.) statistic to test the one sample block-matrix sphericity hypothesis are developed under the assumption of multivariate normality. Using a decomposition of the null hypothesis in two null hypotheses, one for testing the independence of the k groups of variables and the other one for testing the equality of the k block d...

The probative value of forensic science evidence (such as a shoeprint) varies widely depending on how the evidence and hypothesis of interest is characterized. This paper uses a likelihood ratio (LR) approach to identify the probative value of forensic science evidence. It argues that the “evidence” component should be characterized as a “reported match,” and that the hypothesis component shoul...

Often a question arises as to whether the observed data are a sample from a homogeneous population or the data have come from a heterogeneous population. In particular, one wants to test for a single normal distribution versus a mixture of two normal distributions. Classic asymptotic results fail to apply to the problem since the model does not satisfy the regularity conditions. This paper inve...

We develop a novel nonparametric likelihood ratio test for independence between two random variables using a technique that is free of the common constraints of defining a given set of specific dependence structures. Our methodology revolves around an exact density-based empirical likelihood ratio test statistic that approximates in a distribution-free fashion the corresponding most powerful pa...

The likelihood ratio statistic for testing pointwise hypotheses about the survival time distribution in the current status model can be inverted to yield confidence intervals. One advantage of this procedure is that confidence intervals can be formed without estimating the unknown parameters that figure in the asymptotic distribution of the MLE of the distribution function. We discuss the likel...

We show that large-scale typicality of Markov sample paths implies that the likelihood ratio statistic satisfies a law of iterated logarithm uniformly to the same scale. As a consequence, the penalized likelihood Markov order estimator is strongly consistent for penalties growing as slowly as log log n when an upper bound is imposed on the order which may grow as rapidly as log n. Our method of...

This paper proposes an asymptotically efficient method for estimating models with conditional moment restrictions. Our estimator generalizes the maximum empirical likelihood estimator (MELE) of Qin and Lawless (1994). Using a kernel smoothing method, we efficiently incorporate the information implied by the conditional moment restrictions into our empirical likelihood-based procedure. This yiel...

The limiting distribution of the quantile estimate for the autoregressive coefficient of a near-integrated first order autoregressive model with infinite variance errors is derived. Since the limiting distribution depends on the unknown density function of the errors, an empirical likelihood ratio statistic is proposed from which confidence intervals can be constructed for the near unit root mo...