Hypothesis testing with fuzzy data: An application to quality control of cheese

2011 ◽  
Author(s):  
Ana Belen Ramos-Guajardo ◽  
Gil Gonzalez-Rodriguez
Keyword(s):  
Quality Control ◽  
Hypothesis Testing ◽  
Fuzzy Data

scholarly journals Quality Control of “As Built” BIM Datasets Using the ISO 19157 Framework and a Multiple Hypothesis Testing Method Based on Proportions

2019 ◽  
Vol 8 (12) ◽  
pp. 569 ◽  
Author(s):  
Francisco Javier Ariza-López ◽  
José Rodríguez-Avi ◽  
Juan Francisco Reinoso-Gordo ◽  
Íñigo Antonio Ariza-López
Keyword(s):  
Quality Control ◽  
Data Quality ◽  
Hypothesis Testing ◽  
Evaluation Method ◽  
Residential Building ◽  
Multiple Hypothesis Testing ◽  
Quality Controls ◽  
Testing Method ◽  
Multiple Hypothesis ◽  
Building Information

Building information model (BIM) data are digital and geometric-based data that are enriched thematically, semantically, and relationally, and are conceptually very similar to geographic information. In this paper, we propose both the use of the international standard ISO 19157 for the adequate formulation of the quality control for BIM datasets and a statistical approach based on a binomial/multinomial or hypergeometric (univariate/multivariate) model and a multiple hypothesis testing method. The use of ISO 19157 means that the definition of data quality units conforms to data quality elements and well-defined scopes, but also that the evaluation method and conformity levels use standardized measures. To achieve an accept/reject decision for quality control, a statistical model is needed. Statistical methods allow one to limit the risks of the parties (producer and user risks). In this way, several statistical models, based on proportions, are proposed and we illustrate how to apply several quality controls together (multiple hypothesis testing). All use cases, where the comparison of a BIM dataset versus reality is needed, are appropriate situations in which to apply this method in order to supply a general digital model of reality. An example of its application is developed to control an “as-built” BIM dataset where sampling is needed. This example refers to a simple residential building with four floors, composed of a basement garage, two commercial premises, four apartments, and an attic. The example is composed of six quality controls that are considered simultaneously. The controls are defined in a rigorous manner using ISO 19157, by means of categories, scopes, data quality elements, quality measures, compliance levels, etc. The example results in the rejection of the BIM dataset. The presented method is, therefore, adequate for controlling BIM datasets.

scholarly journals Bootstrap Statistical Inference for the Variance Based on Fuzzy Data

2016 ◽  
Vol 38 (2) ◽  
Author(s):  
Mohammad Ghasem Akbari ◽  
Abdolhamid Rezaei
Keyword(s):  
Hypothesis Testing ◽  
Statistical Inference ◽  
Confidence Intervals ◽  
Nonparametric Estimation ◽  
Bootstrap Method ◽  
Estimation Problem ◽  
Fuzzy Data ◽  
Signed Distance ◽  
Straightforward Method ◽  
Statistical Hypotheses

The bootstrap is a simple and straightforward method for calculating approximated biases, standard deviations, confidence intervals, testing statistical hypotheses, and so forth, in almost any nonparametric estimation problem. In this paper we describe a bootstrap method for variance that is designed directly for hypothesis testing in case of fuzzy data based on Yao-Wu signed distance.

Bootstrap techniques and fuzzy random variables: Synergy in hypothesis testing with fuzzy data

2006 ◽  
Vol 157 (19) ◽  
pp. 2608-2613 ◽  
Author(s):  
Gil González-Rodríguez ◽  
Manuel Montenegro ◽  
Ana Colubi ◽  
María Ángeles Gil
Keyword(s):  
Hypothesis Testing ◽  
Random Variables ◽  
Fuzzy Data ◽  
Fuzzy Random Variables ◽  
Bootstrap Techniques ◽  
Fuzzy Random

scholarly journals A hypothesis testing-based discussion on the sensitivity of means of fuzzy data with respect to data shape

2017 ◽  
Vol 328 ◽  
pp. 54-69 ◽  
Author(s):  
María Asunción Lubiano ◽  
Antonia Salas ◽  
María Ángeles Gil
Keyword(s):  
Hypothesis Testing ◽  
Fuzzy Data

scholarly journals Quality Control in SBAS: Protection Levels and Reliability Levels

2009 ◽  
Vol 62 (3) ◽  
pp. 509-522 ◽  
Author(s):  
João Oliveira ◽  
Christian Tiberius
Keyword(s):  
Quality Control ◽  
Hypothesis Testing ◽  
Statistical Hypothesis ◽  
Statistical Hypothesis Testing ◽  
The Common ◽  
Position Solution

This contribution extends the common documented approach of integrity through Protection Levels in Satellite-Based Augmentation System (SBAS) positioning for aeronautics, to reliability on the basis of statistical hypothesis testing, and as such provides a safeguard against model misspecifications as anomalies and outliers in the measurements. It is shown that when integrity is monitored through Protection Levels and reliability added through Reliability Levels, the availability of the SBAS position solution is more than 99% for APV-I precision approach. The availability for CAT-I is currently just a few percent. When the Galileo constellation is added, and current performance is copied ahead, the percentage for CAT-I increases to beyond 95%.

Health communication ethics and CDC quality-control guidelines for information

2003 ◽  
Vol 118 (3) ◽  
pp. 193-196 ◽  
Author(s):  
Jeffrey W McKenna ◽  
Terry F Pechacek ◽  
Donna F Stroup
Keyword(s):  
Quality Control ◽  
Health Communication ◽  
Communication Ethics
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