(Statistics, Probability and Chaos): Rejoinder (Part 2)

  title={(Statistics, Probability and Chaos): Rejoinder (Part 2)},
  author={L. Mark Berliner},
  journal={Statistical Science},
Serinaldi F, Kilsby CG, Lombardo F. Untenable nonstationarity: An assessment of the fitness for purpose of trend
The detection and attribution of long-term patterns in hydrological time series have been important research topics for decades. A significant portion of the literature regards such patterns as
Statistical analysis of Bernoulli, logistic, and tent maps with applications to radar signal design
The uniqueness of the Bernoulli frequency modulated signal, and other chaos-based FM signals, can be exploited to improve the performance of the Synthetic Aperture Radar systems. Recent work suggests
Neural Network Data Analysis Using Simulnet™
The book presents an introduction to the analysis of data using neural networks, which includes multilayer feed-forward networks using error back propagation, genetic algorithm-neural network hybrids, generalized regression neural Networks, learning quantizer networks, and self-organizing feature maps.


Threshold-range scaling of excitable cellular automata
This paper describes a framework, theoretically based, but relying on extensive interactive computer graphics experimentation, for investigation of the complex dynamics shared by excitable media in a broad spectrum of scientific contexts by focusing on simple mathematical prototypes to obtain a better understanding of the basic organizational principles underlying spatially distributed oscillating systems.
Short distances, flat triangles and Poisson limits
Motivated by problems in the analysis of spatial data, we prove some general Poisson limit theorems for the U-statistics of Hoeffding (1948). The theorems are applied to tests of clustering or
Dimension of invariant measures for maps with exponent zero
Abstract We give examples of maps of the interval with zero entropy for which the continuous invariant measure has no dimension, and we prove a dimension property for maps lying in the stable