The Experts below are selected from a list of 5655 Experts worldwide ranked by ideXlab platform
Yin Yao Shugart - One of the best experts on this subject based on the ideXlab platform.
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Covariate-based linkage analysis: application of a propensity score as the Single Covariate consistently improves power to detect linkage
European journal of human genetics : EJHG, 2006Co-Authors: Betty Q. Doan, Alexa J.m. Sorant, Constantine Frangakis, Joan E. Bailey-wilson, Yin Yao ShugartAbstract:Successful identification of genetic risk loci for complex diseases has relied on the ability to minimize disease and genetic heterogeneity to increase the power to detect linkage. One means to account for disease heterogeneity is by incorporating Covariate data. However, the inclusion of each Covariate will add one degree of freedom to the allele sharing based linkage test, which may in fact decrease power. We explore the application of a propensity score, which is typically used in causal inference to combine multiple Covariates into a Single variable, as a means of allowing for multiple Covariates with the addition of only one degree of freedom. In this study, binary trait data, simulated under various models involving genetic and environmental effects, were analyzed using a nonparametric linkage statistic implemented in LODPAL. Power and type I error rates were evaluated. Results suggest that the use of the propensity score to combine multiple Covariates as a Single Covariate consistently improves the power compared to an analysis including no Covariates, each Covariate individually, or all Covariates simultaneously. Type I error rates were inflated for analyses with Covariates and increased with increasing number of Covariates, but reduced to nominal rates with sample sizes of 1000 families. Therefore, we recommend using the propensity score as a Single Covariate in the linkage analysis of a trait suspected to be influenced by multiple Covariates because of its potential to increase the power to detect linkage, while controlling for the increase in the type I error.
Mikel Aickin - One of the best experts on this subject based on the ideXlab platform.
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effect of design adaptive allocation on inference for a regression parameter two group Single Covariate and double Covariate cases
Statistics & Probability Letters, 2009Co-Authors: Mikel AickinAbstract:Abstract Assignment to treatment group by randomization has been advocated with great success in biomedical trials. Research on optimal experimental design suggests, however, that it should be possible to obtain efficiency gains over randomization by balancing treatment groups with regard to prognostic factors. The only practical way of doing this involves sequential allocation to treatment that evolves during the recruitment period, but any such method has been questioned on the grounds that statistical inference using the estimated treatment effect is suspect. Results reported here show by means of a regression simulation that the estimate obtained from a dynamically balanced trial is unbiased, and a new estimate of its standard deviation is similarly shown to be unbiased. If one does not adjust for the balancing factors in the analysis, then randomization is frequently unacceptably inefficient. If one does adjust, then the efficiency advantage of balancing is modest on average, but still important in an appreciable fraction of trials with small sample sizes.
Sun Lijun - One of the best experts on this subject based on the ideXlab platform.
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Incorporating travel behavior regularity into passenger flow forecasting
'Elsevier BV', 2021Co-Authors: Cheng Zanhong, Trepanier Martin, Sun LijunAbstract:Accurate forecasting of passenger flow (i.e., ridership) is critical to the operation of urban metro systems. Previous studies mainly model passenger flow as time series by aggregating individual trips and then perform forecasting based on the values in the past several steps. However, this approach essentially overlooks the fact that passenger flow consists of trips from each individual traveler. For example, a traveler’s work trip in the morning can help predict his/her home trip in the evening, while this causal structure cannot be explicitly encoded in standard time series models. In this paper, we propose a new forecasting framework for boarding flow by incorporating the generative mechanism into standard time series models and leveraging the strong regularity rooted in travel behavior. In doing so, we introduce returning flow from previous alighting trips as a new Covariate, which captures the causal structure and long-range dependencies in passenger flow data based on travel behavior. We develop the return probability parallelogram (RPP) to summarize the causal relationships and estimate the return flow. The proposed framework is evaluated using real-world passenger flow data, and the results confirm that the returning flow—a Single Covariate—can substantially and consistently improve various forecasting tasks, including one-step ahead forecasting, multi-step ahead forecasting, and forecasting under special events. And the proposed method is more effective for business-type stations with most passengers come and return within the same day. This study can be extended to other modes of transport, and it also sheds new light on general demand time series forecasting problems, in which causal structure and long-range dependencies are generated by the user behavior
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Incorporating travel behavior regularity into passenger flow forecasting
2020Co-Authors: Cheng Zhanhong, Trepanier Martin, Sun LijunAbstract:Accurate forecasting of passenger flow (i.e., ridership) is critical to the operation of urban metro systems. Previous studies mainly model passenger flow as time series by aggregating individual trips and then perform forecasting based on the values in the past several steps. However, this approach essentially overlooks the fact that passenger flow consists of trips from each individual traveler with strong regularity rooted in their travel behavior. For example, a traveler's work trip in the morning can help predict his/her home trip in the evening, while this fact cannot be explicitly encoded in standard time series models. In this paper, we propose a new passenger flow forecasting framework by incorporating the generative mechanism into standard time series models. In doing so, we focus on forecasting boarding demand, and we introduce returning flow from previous alighting trips as a new Covariate, which captures the causal structure and long-range dependencies in passenger flow data based on travel behavior. We develop the return probability parallelogram (RPP) to summarize the causal relationships and estimate the return flow. The proposed framework is evaluated using real-world passenger flow data, and the results confirm that the returning flow---a Single Covariate---can substantially and consistently benefit various forecasting tasks, including one-step ahead forecasting, multi-step ahead forecasting, and forecasting under special events. This study can be extended to other modes of transport, and it also sheds new light on general demand time series forecasting problems, in which causal structure and the long-range dependencies are generated by the behavior patterns of users
Betty Q. Doan - One of the best experts on this subject based on the ideXlab platform.
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Covariate-based linkage analysis: application of a propensity score as the Single Covariate consistently improves power to detect linkage
European journal of human genetics : EJHG, 2006Co-Authors: Betty Q. Doan, Alexa J.m. Sorant, Constantine Frangakis, Joan E. Bailey-wilson, Yin Yao ShugartAbstract:Successful identification of genetic risk loci for complex diseases has relied on the ability to minimize disease and genetic heterogeneity to increase the power to detect linkage. One means to account for disease heterogeneity is by incorporating Covariate data. However, the inclusion of each Covariate will add one degree of freedom to the allele sharing based linkage test, which may in fact decrease power. We explore the application of a propensity score, which is typically used in causal inference to combine multiple Covariates into a Single variable, as a means of allowing for multiple Covariates with the addition of only one degree of freedom. In this study, binary trait data, simulated under various models involving genetic and environmental effects, were analyzed using a nonparametric linkage statistic implemented in LODPAL. Power and type I error rates were evaluated. Results suggest that the use of the propensity score to combine multiple Covariates as a Single Covariate consistently improves the power compared to an analysis including no Covariates, each Covariate individually, or all Covariates simultaneously. Type I error rates were inflated for analyses with Covariates and increased with increasing number of Covariates, but reduced to nominal rates with sample sizes of 1000 families. Therefore, we recommend using the propensity score as a Single Covariate in the linkage analysis of a trait suspected to be influenced by multiple Covariates because of its potential to increase the power to detect linkage, while controlling for the increase in the type I error.
Alexa J.m. Sorant - One of the best experts on this subject based on the ideXlab platform.
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Covariate-based linkage analysis: application of a propensity score as the Single Covariate consistently improves power to detect linkage
European journal of human genetics : EJHG, 2006Co-Authors: Betty Q. Doan, Alexa J.m. Sorant, Constantine Frangakis, Joan E. Bailey-wilson, Yin Yao ShugartAbstract:Successful identification of genetic risk loci for complex diseases has relied on the ability to minimize disease and genetic heterogeneity to increase the power to detect linkage. One means to account for disease heterogeneity is by incorporating Covariate data. However, the inclusion of each Covariate will add one degree of freedom to the allele sharing based linkage test, which may in fact decrease power. We explore the application of a propensity score, which is typically used in causal inference to combine multiple Covariates into a Single variable, as a means of allowing for multiple Covariates with the addition of only one degree of freedom. In this study, binary trait data, simulated under various models involving genetic and environmental effects, were analyzed using a nonparametric linkage statistic implemented in LODPAL. Power and type I error rates were evaluated. Results suggest that the use of the propensity score to combine multiple Covariates as a Single Covariate consistently improves the power compared to an analysis including no Covariates, each Covariate individually, or all Covariates simultaneously. Type I error rates were inflated for analyses with Covariates and increased with increasing number of Covariates, but reduced to nominal rates with sample sizes of 1000 families. Therefore, we recommend using the propensity score as a Single Covariate in the linkage analysis of a trait suspected to be influenced by multiple Covariates because of its potential to increase the power to detect linkage, while controlling for the increase in the type I error.
