Location-scale invariant Bickel-Rosenblatt goodness-of-fit tests (IBR tests) are considered in this paper to test the hypothesis that f , the common density function of the observed independent d-dimensional random vectors, belongs to a null location-scale family of density functions. The asymptotic behaviour of the test procedures for fixed and non-fixed bandwidths is studied by using an unify...

We consider the problem of estimating the proportion θ of true null hypotheses in a multiple testing context. The setup is classically modeled through a semiparametric mixture with two components: a uniform distribution on interval [0, 1] with prior probability θ and a nonparametric density f . We discuss asymptotic efficiency results and establish that two different cases occur whether f vanis...

Abstract A perennial objection against Bayes factor point-null hypothesis tests is that the known to be false from outset. We examine consequences of approximating sharp by a hazy ‘peri-null’ instantiated as narrow prior distribution centered on point interest. The peri-null then equals multiplied correction term which itself factor. For moderate sample sizes, relatively inconsequential; howeve...

This article proposes the asymmetric linear double autoregression, which jointly models conditional mean and heteroscedasticity characterized by effects. A sufficient condition is established for existence of a strictly stationary solution. With quasi-maximum likelihood estimation (QMLE) procedure introduced, Bayesian information criterion (BIC) its modified version are proposed model selection...

We study the asymptotic stabilization of the origin for the two-dimensional (2-D) Euler equation of incompressible inviscid fluid in a bounded domain. We assume that the controls act on an arbitrarily small nonempty open subset of the boundary. We prove the null global asymptotic stabilizability by means of explicit feedback laws if the domain is connected and simply connected.

For goodness-of-fit tests for a model (distribution) admitting nuisance location or scale parameters, second-order asymptotic distributional representations are exploited for some robust estimators that are asymptotically first-order equivalent; their difference then provides a goodness-of-fit test criterion, whose asymptotic properties are studied under the null hypothesis as well as under loc...

This study considers the goodness of fit test for a class of conditionally heteroscedastic location-scale time series models. For this task, we develop an entropy-type goodness of fit test based on residuals. To examine the asymptotic behavior of the test, we first investigate the asymptotic property of the residual empirical process and then derive the limiting null distribution of the entropy...

A construction of conformal infinity in null and spatial directions is constructed for the Rainbow-flat space-time corresponding to doubly special relativity. From this construction a definition of asymptotic DSRness is put forward which is compatible with the correspondence principle of Rainbow Gravity. Furthermore a result equating asymptotically flat space-times with asymptotically DSR space...

Asymptotic and bootstrap tests are studied for testing whether there is a relation of stochastic dominance between two distributions. These tests have a null hypothesis of nondominance, with the advantage that, if this null is rejected, then all that is left is dominance. This also leads us to define and focus on restricted stochastic dominance, the only empirically useful form of dominance rel...

ABSTRACT There has been growing interest in exploiting potential forecast gains from the nonlinear structure of self-exciting threshold autoregressive (SETAR) models. Statistical tests have been proposed in the literature to help analysts check for the presence of SETAR-type nonlinearities in observed time series. However, previous studies show that classical nonlinearity tests are not robust t...