Assessing geographic variations in health events is one of the major tasks in spatial epidemiologic studies. Geographic variation in a health event can be estimated using the neighborhood-level variance that is derive...Assessing geographic variations in health events is one of the major tasks in spatial epidemiologic studies. Geographic variation in a health event can be estimated using the neighborhood-level variance that is derived from a generalized mixed linear model or a Bayesian spatial hierarchical model. Two novel heterogeneity measures, including median odds ratio and interquartile odds ratio, have been developed to quantify the magnitude of geographic variations and facilitate the data interpretation. However, the statistical significance of geographic heterogeneity measures was inaccurately estimated in previous epidemiologic studies that reported two-sided 95% confidence intervals based on standard error of the variance or 95% credible intervals with a range from 2.5th to 97.5th percentiles of the Bayesian posterior distribution. Given the mathematical algorithms of heterogeneity measures, the statistical significance of geographic variation should be evaluated using a one-tailed P value. Therefore, previous studies using two-tailed 95% confidence intervals based on a standard error of the variance may have underestimated the geographic variation in events of their interest and those using 95% Bayesian credible intervals may need to re-evaluate the geographic variation of their study outcomes.展开更多
This paper presents a statistically refined Bouc-Wen model of tri-axial interactions for the identification of structural systems under tri-directional seismic excitations. Through limited vibration measurements in th...This paper presents a statistically refined Bouc-Wen model of tri-axial interactions for the identification of structural systems under tri-directional seismic excitations. Through limited vibration measurements in the National Center for Research on Earthquake Engineering in Taiwan conducting model-based experiments, the 3-D Bouc-Wen model has been statistically and repetitively refined using the 95% confidence interval of the estimated structural parameters to determine their statistical significance in a multiple regression setting. When the parameters＇ confidence interval covers the ＂null＂ value, it is statistically sustainable to truncate such parameters. The remaining parameters will repetitively undergo such parameter sifting process for model refinement until all the parameters＇ statistical significance cannot be further improved. The effectiveness of the refined model has been shown considering the effects of sampling errors, of coupled restoring forces in tri-directions, and of the under-over-parameterization of structural systems. Sifted and estimated parameters such as the stiffness, and its corresponding natural frequency, resulting from the identification methodology developed in this study are carefully observed for system vibration control.展开更多
文摘Assessing geographic variations in health events is one of the major tasks in spatial epidemiologic studies. Geographic variation in a health event can be estimated using the neighborhood-level variance that is derived from a generalized mixed linear model or a Bayesian spatial hierarchical model. Two novel heterogeneity measures, including median odds ratio and interquartile odds ratio, have been developed to quantify the magnitude of geographic variations and facilitate the data interpretation. However, the statistical significance of geographic heterogeneity measures was inaccurately estimated in previous epidemiologic studies that reported two-sided 95% confidence intervals based on standard error of the variance or 95% credible intervals with a range from 2.5th to 97.5th percentiles of the Bayesian posterior distribution. Given the mathematical algorithms of heterogeneity measures, the statistical significance of geographic variation should be evaluated using a one-tailed P value. Therefore, previous studies using two-tailed 95% confidence intervals based on a standard error of the variance may have underestimated the geographic variation in events of their interest and those using 95% Bayesian credible intervals may need to re-evaluate the geographic variation of their study outcomes.
文摘This paper presents a statistically refined Bouc-Wen model of tri-axial interactions for the identification of structural systems under tri-directional seismic excitations. Through limited vibration measurements in the National Center for Research on Earthquake Engineering in Taiwan conducting model-based experiments, the 3-D Bouc-Wen model has been statistically and repetitively refined using the 95% confidence interval of the estimated structural parameters to determine their statistical significance in a multiple regression setting. When the parameters＇ confidence interval covers the ＂null＂ value, it is statistically sustainable to truncate such parameters. The remaining parameters will repetitively undergo such parameter sifting process for model refinement until all the parameters＇ statistical significance cannot be further improved. The effectiveness of the refined model has been shown considering the effects of sampling errors, of coupled restoring forces in tri-directions, and of the under-over-parameterization of structural systems. Sifted and estimated parameters such as the stiffness, and its corresponding natural frequency, resulting from the identification methodology developed in this study are carefully observed for system vibration control.
