Journal Article10.1016/J.SPL.2016.01.025
Multivariate Poisson interpoint distances
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TL;DR: In this article, the properties of the squared interpoint distances (IDs) in samples taken from multivariate Poisson distributions were studied and the means and covariances of the average IDs were derived.
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About: This article is published in Statistics & Probability Letters. The article was published on 01 May 2016. The article focuses on the topics: Poisson distribution & Multivariate statistics.
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Citations
Interpoint Distance Classification of High Dimensional Discrete Observations
Lingzhe Guo,Reza Modarres +1 more
TL;DR: This work proposes a modification of a test‐based rule to use relative values with respect to the training samples baseline, and compares the proposed rule with parametric methods, and non‐parametric techniques such as support vector machine, nearest neighbour and depth‐based classification.
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M statistic commands: Interpoint distance distribution analysis
Pietro Tebaldi,Marco Bonetti,Marcello Pagano +2 more
- 01 Jan 2010
TL;DR: In this article, the M statistic is used to compare the interpoint distance distribution across groups of observations in a k-dimensional setting, where the locations are distributed in a region of the plane.
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Instance-Based Classification Through Hypothesis Testing
TL;DR: Zhang et al. as mentioned in this paper formulated the binary classification problem as a two-sample testing problem and proposed an instance-based classifier based on hypothesis testing, which can handle outlying instances and control the false discovery rate of test instances assigned to each class.
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Instance-Based Classification through Hypothesis Testing.
TL;DR: The presented classification method can be regarded as an instance-based classifier based on hypothesis testing and is able to achieve the same level performance as several classic classifiers and has significantly better performance than existing testing- based classifiers.
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References
A spatial scan statistic
TL;DR: In this article, a spatial scan statistic for the detection of clusters in a multi-dimensional point process is proposed, where the area of the scanning window is allowed to vary, and the baseline process may be any inhomogeneous Poisson process or Bernoulli process with intensity pro-portional to some known function.
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