Multivariate hypothesis testing and its application to quality control

1987 ◽  
Vol 39 (2) ◽  
pp. 2648-2661 ◽  
Author(s):  
O. I. Teskin ◽  
R. S. Sudakov
Keyword(s):  
Quality Control ◽  
Hypothesis Testing ◽  
Multivariate Hypothesis Testing

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.

A multivariate hypothesis testing framework for tissue clustering and classification of DTI data

2009 ◽  
Vol 22 (7) ◽  
pp. 716-729 ◽  
Author(s):  
Raisa Z. Freidlin ◽  
Evren Özarslan ◽  
Yaniv Assaf ◽  
Michal E. Komlosh ◽  
Peter J. Basser
Keyword(s):  
Hypothesis Testing ◽  
Testing Framework ◽  
Clustering And Classification ◽  
Multivariate Hypothesis Testing

Measuring community shifts in a weed seedbank study with the use of distance-based redundancy analysis

Weed Science ◽  
2006 ◽  
Vol 54 (5) ◽  
pp. 861-866 ◽  
Author(s):  
Chris Reberg-Horton ◽  
Eric R. Gallandt ◽  
Tom Molloy
Keyword(s):  
Hypothesis Testing ◽  
Pest Management ◽  
Redundancy Analysis ◽  
Soil Amendments ◽  
Soil Management ◽  
Data Interpretation ◽  
Management Systems ◽  
Long Term Study ◽  
Entry Points ◽  
Multivariate Hypothesis Testing

Distance-based redundancy analysis (db-RDA), a recently developed ordination technique useful for both multivariate hypothesis testing and data interpretation, was used to evaluate treatment effects on weed communities in a long-term study of alternative potato cropping systems. The experiment consisted of a factorial arrangement of three pest management systems, conventional (CON), reduced input (RI), and biointensive (BIO), two soil management systems (amended vs. unamended), and two crop-rotation entry points. Soil samples collected in the spring of 1998 were subjected to exhaustive germination as a means of characterizing the weed community. Using partial ordinations, each factor in the factorial treatment structure was tested separately, revealing a significant interaction between pest and soil management systems. An ordination diagram of the pest by soil management interaction was used to interpret the results. Weed species that were highly correlated with the first two ordination axes included: common lambsquarters, broadleaf plantain, oakleaf goosefoot, common hempnettle and a complex of the Brassicaceae that included wild mustard, birdsrape mustard, and wild radish. Univariate analyses confirmed the response of these species to the factors examined. The BIO pest management system showed a different response to soil amendments than the other systems. Soil amendments caused an increase in the total weed density in the CON and RI systems, but caused a decrease in the BIO system. Given the need for better multivariate hypothesis testing and data interpretation in many types of weed science research, the use of db-RDA is expected to grow.

scholarly journals Statistical group comparison of diffusion tensors via multivariate hypothesis testing

2007 ◽  
Vol 57 (6) ◽  
pp. 1065-1074 ◽  
Author(s):  
Brandon Whitcher ◽  
Jonathan J. Wisco ◽  
Nouchine Hadjikhani ◽  
David S. Tuch
Keyword(s):  
Hypothesis Testing ◽  
Group Comparison ◽  
Diffusion Tensors ◽  
Multivariate Hypothesis Testing
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