scholarly journals Weighted Overlap Dominance – A procedure for interactive selection on multidimensional interval data

2011 ◽  
Vol 35 (8) ◽  
pp. 3958-3969 ◽  
Author(s):  
Jens Leth Hougaard ◽  
Kurt Nielsen
Keyword(s):  
Interval Data ◽  
Interactive Selection

WSN Localization Method Using Interval Data Clustering

2012 ◽  
Vol 38 (7) ◽  
pp. 1190 ◽  
Author(s):  
Yu PENG ◽  
Qing-Hua LUO ◽  
Dan WANG ◽  
Xi-Yuan PENG
Keyword(s):  
Data Clustering ◽  
Interval Data ◽  
Localization Method

scholarly journals Water Particles Monitoring in the Atacama Desert: SPC Approach Based on Proportional Data

Axioms ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 154
Author(s):  
Anderson Fonseca ◽  
Paulo Henrique Ferreira ◽  
Diego Carvalho do Nascimento ◽  
Rosemeire Fiaccone ◽  
Christopher Ulloa-Correa ◽  
...  
Keyword(s):  
Control Chart ◽  
Average Run Length ◽  
Atacama Desert ◽  
Real Data ◽  
Interval Data ◽  
Total Quality ◽  
Statistical Process ◽  
Monte Carlo Simulation Study ◽  
Run Length ◽  
Study Results

Statistical monitoring tools are well established in the literature, creating organizational cultures such as Six Sigma or Total Quality Management. Nevertheless, most of this literature is based on the normality assumption, e.g., based on the law of large numbers, and brings limitations towards truncated processes as open questions in this field. This work was motivated by the register of elements related to the water particles monitoring (relative humidity), an important source of moisture for the Copiapó watershed, and the Atacama region of Chile (the Atacama Desert), and presenting high asymmetry for rates and proportions data. This paper proposes a new control chart for interval data about rates and proportions (symbolic interval data) when they are not results of a Bernoulli process. The unit-Lindley distribution has many interesting properties, such as having only one parameter, from which we develop the unit-Lindley chart for both classical and symbolic data. The performance of the proposed control chart is analyzed using the average run length (ARL), median run length (MRL), and standard deviation of the run length (SDRL) metrics calculated through an extensive Monte Carlo simulation study. Results from the real data applications reveal the tool’s potential to be adopted to estimate the control limits in a Statistical Process Control (SPC) framework.

scholarly journals IGD: high-performance search for large-scale genomic interval datasets

2020 ◽  
Author(s):  
Jianglin Feng ◽  
Nathan C Sheffield
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Interval Data ◽  
Scale Analysis ◽  
Genome Database ◽  
Genomic Interval ◽  
Critical Resource ◽  
Genomic Regions ◽  
Genome Projects

Abstract Summary Databases of large-scale genome projects now contain thousands of genomic interval datasets. These data are a critical resource for understanding the function of DNA. However, our ability to examine and integrate interval data of this scale is limited. Here, we introduce the integrated genome database (IGD), a method and tool for searching genome interval datasets more than three orders of magnitude faster than existing approaches, while using only one hundredth of the memory. IGD uses a novel linear binning method that allows us to scale analysis to billions of genomic regions. Availability https://github.com/databio/IGD

Building perceptual color maps for visualizing interval data

2000 ◽  
Author(s):  
Alan D. Kalvin ◽  
Bernice E. Rogowitz ◽  
Adar Pelah ◽  
Aron Cohen
Keyword(s):  
Interval Data

Functional Canonical Analysis Between Functional and Interval Data

2009 ◽  
Author(s):  
Yow-Jen Jou ◽  
Chien-Chia Huang ◽  
Jennifer Yuh-Jen Wu ◽  
George Maroulis ◽  
Theodore E. Simos
Keyword(s):  
Interval Data ◽  
Canonical Analysis

scholarly journals Innovative Correlation Coefficient Measurement with Fuzzy Data

2016 ◽  
Vol 2016 ◽  
pp. 1-11
Author(s):  
Berlin Wu ◽  
Chin Feng Hung
Keyword(s):  
Mathematics Achievement ◽  
Correlation Coefficient ◽  
Empirical Studies ◽  
Correlation Coefficients ◽  
Interval Data ◽  
Fuzzy Data ◽  
Pearson’S Correlation Coefficient ◽  
Coefficient Measurement ◽  
The Relationship ◽  
Fuzzy Interval

Correlation coefficients are commonly found with crisp data. In this paper, we use Pearson’s correlation coefficient and propose a method for evaluating correlation coefficients for fuzzy interval data. Our empirical studies involve the relationship between mathematics achievement and other projects.

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