Next Article in Journal
Long Colimits of Topological Groups III: Homeomorphisms of Products and Coproducts
Next Article in Special Issue
A Method for Visualizing Posterior Probit Model Uncertainty in the Early Prediction of Fraud for Sustainability Development
Previous Article in Journal
A Tseng-Type Algorithm with Self-Adaptive Techniques for Solving the Split Problem of Fixed Points and Pseudomonotone Variational Inequalities in Hilbert Spaces
Article

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

1
Department of Statistics, Federal University of Bahia, Salvador 40170110, Brazil
2
Departamento de Matemática, Facultad de Ingeniería, Universidad de Atacama, Copiapó 1530000, Chile
3
Laboratorio de Investigación de la Criósfera y Aguas, IDICTEC, Universidad de Atacama, Copiapó 1530000, Chile
4
Institute of Mathematical and Computer Sciences, University of São Paulo, São Carlos 13566590, Brazil
*
Author to whom correspondence should be addressed.
Academic Editor: Hari Mohan Srivastava
Axioms 2021, 10(3), 154; https://doi.org/10.3390/axioms10030154
Received: 15 June 2021 / Revised: 6 July 2021 / Accepted: 8 July 2021 / Published: 13 July 2021
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. View Full-Text
Keywords: Symbolic Data Analysis (SDA) in Statistical Process Control (SPC); rates and proportions data; unit-Lindley distribution; relative air humidity monitoring; Monte Carlo simulation Symbolic Data Analysis (SDA) in Statistical Process Control (SPC); rates and proportions data; unit-Lindley distribution; relative air humidity monitoring; Monte Carlo simulation
Show Figures

Figure 1

MDPI and ACS Style

Fonseca, A.; Ferreira, P.H.; Nascimento, D.C.d.; Fiaccone, R.; Ulloa-Correa, C.; García-Piña, A.; Louzada, F. Water Particles Monitoring in the Atacama Desert: SPC Approach Based on Proportional Data. Axioms 2021, 10, 154. https://doi.org/10.3390/axioms10030154

AMA Style

Fonseca A, Ferreira PH, Nascimento DCd, Fiaccone R, Ulloa-Correa C, García-Piña A, Louzada F. Water Particles Monitoring in the Atacama Desert: SPC Approach Based on Proportional Data. Axioms. 2021; 10(3):154. https://doi.org/10.3390/axioms10030154

Chicago/Turabian Style

Fonseca, Anderson, Paulo H. Ferreira, Diego C.d. Nascimento, Rosemeire Fiaccone, Christopher Ulloa-Correa, Ayón García-Piña, and Francisco Louzada. 2021. "Water Particles Monitoring in the Atacama Desert: SPC Approach Based on Proportional Data" Axioms 10, no. 3: 154. https://doi.org/10.3390/axioms10030154

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop