Robust Asymptotic Tests of Statistical Hypotheses Involving Nuisance Parameters

Moran–Flavoured Tests with Nuisance Parameters: Examples

In this paper I illustrate the use of general conditions, derived by Pinkse (1999), under which Moran–flavoured tests for spatial correlation and spatial cross–correlation have a limiting normal distribution in the presence of nuisance parameters in six frequently encountered spatial models. The conditions are weaker than those in related work by other authors and the class of nuisance paramete...

Asymptotic Efficiency of Estimates for Models with Incidental Nuisance Parameters

In this paper we show that the well–known asymptotic efficiency bounds for full mixture models remain valid if individual sequences of nuisance parameters are considered. This is made precise both for some classes of random (i.i.d.) and non–random nuisance parameters. For the random case it is shown that superefficiency of the kind given by an example of Pfanzagl, [7], can happen only with low ...

Robust tests for testing the parameters of a normal population

This article aims to provide a simple robust method to test the parameters of a normal population by using the new diagnostic tool called the “Forward Search” (FS) method. The most commonly used procedures to test the mean and variance of a normal distribution are Student’s t test and Chi-square test, respectively. These tests suffer from the presence of outliers. We introduce the FS version of...

Targeted estimation of nuisance parameters to obtain valid statistical inference.

In order to obtain concrete results, we focus on estimation of the treatment specific mean, controlling for all measured baseline covariates, based on observing independent and identically distributed copies of a random variable consisting of baseline covariates, a subsequently assigned binary treatment, and a final outcome. The statistical model only assumes possible restrictions on the condit...

Statistical Tests Involving Several Independent Gamma Distributions