In this paper we introduce a generalization of the Neyman-Scott process (Neyman and Scott, 1958) that allows for regularity in the parent process. In particular, we consider the special case where the parent process is a Strauss process with offspring points dispersed about the parent points. Such a generalization allows for point realizations that show a mix of regularity and clustering in the...

In practical signal detection scenarios, parameters of a random process are often uncertain. In this paper, we model such uncertainties as fuzzy parameters of a stationary random process. A fuzzy Neyman-Pearson hypothesis test concept which accepts any number of fuzzy parameters is presented. A suitable decision rule is developed by applying theory for ordering fuzzy numbers, and stated in term...

A new processor for detecting a radar target in correlated, non-Gaussian noise is obtained. When this processor and a matched filter are excited with this noise, performance is improved over that of the matched filter alone. The processor is obtained by developing an approximate, bivariate, probability density for the noise and constructing a Neyman-Pearson test and then using an approximation ...

A physical optics formalism is used to establish a first-principles analysis for discriminating specular returns from diffuse returns in a 1-D synthetic aperture radar. The optimum Neyman-Pearson detection processor is shown to substantially outperform the conventional, full-resolution SAR imager for extended specular targets embedded in diffuse clutter plus receiver noise. Significant performa...

Building upon the statistical framework of causal inference, we consider classical approaches to statistical analysis of randomized experiments. The use of randomized experiments in scientific inquiry goes back at least to the 19th century, but formal statistical methods to analyze these experiments were developed in early 20th century by J. Neyman and R. Fisher. The methods they developed were...