Discrete Data Analysis and Point Processes

2010 ◽  
pp. 293-333
Keyword(s):  
Data Analysis ◽  
Point Processes ◽  
Discrete Data

Discrete Data Analysis with R

2015 ◽  
Author(s):  
Michael Friendly ◽  
David Meyer ◽  
Achim Zeileis
Keyword(s):  
Data Analysis ◽  
Discrete Data

scholarly journals A topological data analysis based classification method for multiple measurements

2019 ◽  
Author(s):  
Henri Riihimäki ◽  
Wojciech Chachólski ◽  
Jakob Theorell ◽  
Jan Hillert ◽  
Ryan Ramanujam
Keyword(s):  
Data Analysis ◽  
Point Processes ◽  
Measurement Data ◽  
Repeated Measurements ◽  
Topological Data Analysis ◽  
Network Graph ◽  
Feature Values ◽  
Using Data ◽  
Data Topology ◽  
Topological Data

AbstractBackgroundMachine learning models for repeated measurements are limited. Using topological data analysis (TDA), we present a classifier for repeated measurements which samples from the data space and builds a network graph based on the data topology. When applying this to two case studies, accuracy exceeds alternative models with additional benefits such as reporting data subsets with high purity along with feature values.ResultsFor 300 examples of 3 tree species, the accuracy reached 80% after 30 datapoints, which was improved to 90% after increased sampling to 400 datapoints. Using data from 100 examples of each of 6 point processes, the classifier achieved 96.8% accuracy. In both datasets, the TDA classifier outperformed an alternative model.ConclusionsThis algorithm and software can be beneficial for repeated measurement data common in biological sciences, as both an accurate classifier and a feature selection tool.

scholarly journals Testing goodness of fit for point processes via topological data analysis

2020 ◽  
Vol 14 (1) ◽  
pp. 1024-1074
Author(s):  
Christophe A. N. Biscio ◽  
Nicolas Chenavier ◽  
Christian Hirsch ◽  
Anne Marie Svane
Keyword(s):  
Data Analysis ◽  
Goodness Of Fit ◽  
Point Processes ◽  
Topological Data Analysis ◽  
Topological Data

Analyse Volleyball Echelon Construction of Shandong Province

2011 ◽  
Vol 467-469 ◽  
pp. 52-56
Author(s):  
Min Hang Liu ◽  
Shu Ping Yuan
Keyword(s):  
Sustainable Development ◽  
Data Analysis ◽  
Discrete Data ◽  
Positive Influence ◽  
Shandong Province ◽  
The Sustainable Development ◽  
Level 3

This study analyses a series of problems on the volleyball team echelon construction of Shandong Province, through a great number of discrete data analysis, and put formula of Yt ＝ Yt-1+[(Yt-1 —Yt-2)+(Yt-2 — Yt-3)+…+(Yt-n —Yt-(n+l))] ／ n into calculation, so as to get the following conclusions: 1. While the number of excellent volleyball teams decreased rapidly, meanwhile, the number of 2nd-line increased steadily year by year, which showed the trend of reverse growth to 1st-line; 2. The number of 3rd-line increased slowly, not significantly change, which fluctuated on a certain level; 3. The growing speed of the number of 3rd-line was lower than the number of 2nd-line. This study will play a positive influence and promote the sustainable development of volleyball through the study of analysing volleyball team echelon construction of Shandong Province.

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