Model diagnostics for detecting and identifying method repeatability outliers in precision studies: application to a homogeneity study under a two-stage nested ANOVA

2016 ◽  
Vol 31 (3) ◽  
pp. 686-699 ◽  
Author(s):  
Stephen J. Walsh ◽  
Zsuzsanna Macsik ◽  
Dariusz Wegrzynek ◽  
Thomas Krieger ◽  
Sergei Boulyga
Keyword(s):  
Statistical Model ◽  
Model Diagnostics ◽  
Homogeneity Study ◽  
Two Stage ◽  
Nested Anova ◽  
Iso 5725

In this paper we illustrate the use of statistical model diagnostics as an approach to identifying method repeatability outliers in precision experiments. We describe how this approach addresses the well-known shortcomings of Cochran’s test which is prescribed for this purpose in ISO 5725-2:1994.

scholarly journals Estimating Spatio-Temporal Variations of PM2.5 Concentrations Using VIIRS-Derived AOD in the Guanzhong Basin, China

2019 ◽  
Vol 11 (22) ◽  
pp. 2679 ◽  
Author(s):  
Kainan Zhang ◽  
Gerrit de Leeuw ◽  
Zhiqiang Yang ◽  
Xingfeng Chen ◽  
Xiaoli Su ◽  
...  
Keyword(s):  
Statistical Model ◽  
Cross Validation ◽  
Temporal Variations ◽  
Coefficient Of Determination ◽  
Prediction Errors ◽  
Two Stage ◽  
Temporal Models ◽  
Spatio Temporal ◽  
Guanzhong Basin ◽  
Gwr Model

Aerosol optical depth (AOD) derived from satellite remote sensing is widely used to estimate surface PM2.5 (dry mass concentration of particles with an in situ aerodynamic diameter smaller than 2.5 µm) concentrations. In this research, a two-stage spatio-temporal statistical model for estimating daily surface PM2.5 concentrations in the Guanzhong Basin of China is proposed, using 6 km × 6 km AOD data available from the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument as the main variable and meteorological factors, land-cover, and population data as auxiliary variables. The model is validated using a cross-validation method. The linear mixed effects (LME) model used in the first stage could be improved by using a geographically weighted regression (GWR) model or the generalized additive model (GAM) in the second stage, and the predictive capability of the GWR model is better than that of GAM. The two-stage spatio-temporal statistical model of LME and GWR successfully captures the temporal and spatial variations. The coefficient of determination (R2), the bias and the root-mean-squared prediction errors (RMSEs) of the model fitting to the two-stage spatio-temporal models of LME and GWR were 0.802, −0.378 µg/m3, and 12.746 µg/m3, respectively, and the model cross-validation results were 0.703, 1.451 µg/m3, and 15.731 µg/m3, respectively. The model prediction maps show that the topography has a strong influence on the spatial distribution of the PM2.5 concentrations in the Guanzhong Basin, and PM2.5 concentrations vary with the seasons. This method can provide reliable PM2.5 predictions to reduce the bias of exposure assessment in air pollution and health research.

Simple robust estimation of uranium isotope ratios in individual particles from LA-ICP-MS measurements

2017 ◽  
Vol 32 (6) ◽  
pp. 1155-1165 ◽  
Author(s):  
Stephen J. Walsh ◽  
Naida Dzigal ◽  
Ernesto Chinea-Cano ◽  
Andreas Limbeck
Keyword(s):  
Statistical Model ◽  
Robust Estimation ◽  
Data Reduction ◽  
Isotope Ratios ◽  
Uranium Isotope ◽  
Model Diagnostics ◽  
Icp Ms ◽  
Individual Particles ◽  
Reduction Approach

Statistical model diagnostics assess the quality of the ablation run and underscore the validity of the data reduction approach.

scholarly journals Forecasting Solar Cycle 25 with a Principled Bayesian Two-stage Statistical Model

2021 ◽  
Vol 5 (8) ◽  
pp. 192
Author(s):  
Youwei Yan ◽  
David C. Stenning ◽  
Vinay L. Kashyap ◽  
Yaming Yu
Keyword(s):  
Solar Cycle ◽  
Statistical Model ◽  
Two Stage

Teacher Expectancy and Mathematics Achievement

1970 ◽  
Vol 1 (2) ◽  
pp. 88-94
Author(s):  
Gary L. Brager
Keyword(s):  
Mathematics Achievement ◽  
Learning Task ◽  
Negative Bias ◽  
Positive Bias ◽  
Two Stage ◽  
Programmed Learning ◽  
Experimenter Bias ◽  
Teacher Expectancy ◽  
Nested Anova ◽  
And Mathematics

The phenomenon of experimenter bias (E-bias) was investigated in a fourth-grade arithmetic lesson by using a modified programmed learning task on division in the five-family (five is one of the factors.) E-bias predicts a relation between teacher expectancies and outcomes despite standardization of tasks. Six schools were randomly assigned across three treatments: positive, negative, and neutral bias. Two teachers from each school were randomly selected. The two-stage nested ANOVA was not significant (p<.20), although the treatment means were ordered positive bias, neutral bias, negative bias (high to low.)

Two-stage self-seeding method for preparation of single crystals of poly(phenylene sulfide)

1989 ◽  
Vol 47 ◽  
pp. 368-369
Author(s):  
Sengshiu Chung ◽  
Peggy Cebe
Keyword(s):  
Single Crystals ◽  
High Performance ◽  
Dilute Solution ◽  
Two Stage ◽  
Annealing Behavior ◽  
High Performance Polymers ◽  
Seeding Method ◽  
Phenylene Sulfide

We are studying the crystallization and annealing behavior of high performance polymers, like poly(p-pheny1ene sulfide) PPS, and poly-(etheretherketone), PEEK. Our purpose is to determine whether PPS, which is similar in many ways to PEEK, undergoes reorganization during annealing. In an effort to address the issue of reorganization, we are studying solution grown single crystals of PPS as model materials.Observation of solution grown PPS crystals has been reported. Even from dilute solution, embrionic spherulites and aggregates were formed. We observe that these morphologies result when solutions containing uncrystallized polymer are cooled. To obtain samples of uniform single crystals, we have used two-stage self seeding and solution replacement techniques.

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