On the statistical model of the two-stage designs in bioequivalence assessment

Vangelis Karalis; Panos Macheras

doi:10.1111/jphp.12164

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

Remote Sensing ◽

10.3390/rs11222679 ◽

2019 ◽

Vol 11 (22) ◽

pp. 2679 ◽

Cited By ~ 5

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.

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

Journal of Analytical Atomic Spectrometry ◽

10.1039/c5ja00380f ◽

2016 ◽

Vol 31 (3) ◽

pp. 686-699 ◽

Cited By ~ 2

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.

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Modeling Clinical Failure in Prostate Cancer Patients Based on a Two-stage Statistical Model

Case Medical Research ◽

10.31525/ct1-nct03979079 ◽

2019 ◽

Author(s):

Keyword(s):

Prostate Cancer ◽

Statistical Model ◽

Cancer Patients ◽

Clinical Failure ◽

Two Stage

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A Time Series Analysis of PM2.5 Concentrations in the Southeastern U.S. Using MAIAC AOD in a Two-stage Spatial Statistical Model

ISEE Conference Abstracts ◽

10.1289/isee.2013.o-3-18-06 ◽

2013 ◽

Vol 2013 (1) ◽

pp. 5439

Author(s):

Xuefei Hu ◽

Lance Waller ◽

Alexei Lyapustin ◽

Yang Liu

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Statistical Model ◽

Two Stage ◽

Series Analysis ◽

Spatial Statistical Model

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

Research Notes of the AAS ◽

10.3847/2515-5172/ac1ea0 ◽

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

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

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100153816 ◽

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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