A Martingale Representation for Matching Estimators Matching estimators are widely used in statistical data analysis. However, the distribution of matching estimators has been derived only for particular cases (Abadie and Imbens, 2006). This article establishes a martingale representation for matching estimators. This representation allows the use of martingale limit theorems to derive the asym...

We consider linear statistics of the scaled zeros of Dirichlet L– functions, and show that the first few moments converge to the Gaussian moments. The number of Gaussian moments depends on the particular statistic considered. The same phenomenon is found in Random Matrix Theory, where we consider linear statistics of scaled eigenphases for matrices in the unitary group. In that case the higher ...

Oil spills cause environmental pollution with a serious threat to local communities and sustainable development. Accidental oil can be modelled as stochastic process where each spill event is described by its spatial locations incidence-time hence allow for space-time cluster analysis. Space-time analysis detect pattern distribution of which useful implementing preventive measures evidence-base...

In the literature on change-point analysis, much attention has been paid to detecting changes in certain marginal characteristics, such as mean, variance, and marginal distribution. For time series data with nonparametric time trend, we study the change-point problem for the autocovariance structure of the unobservable error process. To derive the asymptotic distribution of the cumulative sum t...

The simultaneous-sequential method was used to test the processing capacity of establishing mean orientation summaries. Four clusters of oriented Gabor patches were presented in the peripheral visual field. One of the clusters had a mean orientation that was tilted either left or right, whereas the mean orientations of the other three clusters were roughly vertical. All four clusters were prese...

In this paper the bimatrix variate beta type IV distribution is derived from independent Wishart distributed matrix variables. We explore specific properties of this distribution which is then used to derive the exact expressions of the densities of the product and ratio of two dependent Wilks’s statistics and to define the bimatrix Kummer-beta type IV distribution.