Optimal Asymptotic Tests for Nakagami Distribution

Asymptotic Distribution-free Tests for Semiparametric Regressions

This article proposes a new general methodology for constructing nonparametric asymptotic distribution-free tests for semiparametric hypotheses in regression models. Tests are based on the difference between the estimated restricted and unrestricted regression errors’ distributions. A suitable integral transformation of this difference renders the tests asymptotically distribution-free, with li...

Asymptotic Distribution of Ui - Lmpr Tests for Restricted Alternatives

For testing against some restricted alternatives, in the light of the most stringency and somewhere most powerful character, the UI-LMPR tests are asymptotically optimal. The asymptotic distribution theory of such UI-LMPR test statistics is developed , and the same is incorporated in the comparative study of the asymptotic powers of the restricted UI-LMPR tests and their unrestricted versions (...

Asymptotic Distribution of Divergence Measure with Applications

Computation of the asymptotic null distribution of goodness-of-fit tests for multi-state models.

We develop an improved approximation to the asymptotic null distribution of the goodness-of-fit tests for panel observed multi-state Markov models (Aguirre-Hernandez and Farewell, Stat Med 21:1899-1911, 2002) and hidden Markov models (Titman and Sharples, Stat Med 27:2177-2195, 2008). By considering the joint distribution of the grouped observed transition counts and the maximum likelihood esti...

The Asymptotic Distribution of Tests for Over-identification in Partially Identified Linear Structural Equations

We study the asymptotic distribution of the statistics suggested by Sargan/Byron/Wegge and Basmann for testing for over-identification in partially identified linear structural equations and derive closed form expressions for their asymptotic densities. These allow us to understand the asymptotic behaviour of these statistics in cases where identification of the structural parameters fails. We ...