The sign of an association measure between two varibles may be strongly affected and even be reversed after marginalization over a backgruoud variable, which is the well-known Yule-Simpson paradox.Odds ratios are stro...The sign of an association measure between two varibles may be strongly affected and even be reversed after marginalization over a backgruoud variable, which is the well-known Yule-Simpson paradox.Odds ratios are strongly collapsible over a background variable if they remain unchanged no matter how the background variable is partially pooled.In this paper, we firstly give some definitions and notations about odds ratios between a dichotomous explanatory variable and a continuous response variable.Then, we present conditions for simple collapsibility of odds ratios.Further, necessary and sufficient conditions are given for strong collapsibility of odds ratios for continuous outcome variable.展开更多
The purpose of this paper is to bring awareness to the general public that certain conditions that occur at a buoy in the Atlantic Basin, such as wind located at the buoy, pressure located at a buoy, water temperature...The purpose of this paper is to bring awareness to the general public that certain conditions that occur at a buoy in the Atlantic Basin, such as wind located at the buoy, pressure located at a buoy, water temperature located at a buoy, atmospheric pressure located at a buoy, may be useful in helping predict when a hurricane could possibly hit the state of Florida in the future. One of the goals of this paper is to bring new statistical methods to investigate and analyze data, which will create better predicable measures in determining when a hurricane will possibly hit the state of Florida. In this paper, the topics of binary logistic regression and multinomial regression modeling are discussed in reference to their outcomes of both the odds ratio and relative risk ratio respectively. The coefficients from these models will show which prospective buoy conditions are possibly more responsible for indication of a storm being present in the Atlantic Basin. In this paper, the data that was used and compiled into a larger data set came from two different sources. First, the hurricane data for the years 1992-2013 came from Unisys Weather site (Atlantic Basin Hurricanes data) and the buoy data has been available from the National Buoy Center. In this paper, the variables of interest are: storm present, buoy wind speed, buoy pressure, buoy atmospheric temperature, buoy water temperature and buoy wind direction. The buoy conditions are the buoy wind, the buoy wind direction, the buoy pressure, buoy atmospheric temperature and the buoy water temperature.展开更多
There are a few statistics testing the homogeneity of odds ratios across strata. Asymptotic statistics lose their power in the “sparse-data” setting. Both asymptotic statistics and exact tests have low power when th...There are a few statistics testing the homogeneity of odds ratios across strata. Asymptotic statistics lose their power in the “sparse-data” setting. Both asymptotic statistics and exact tests have low power when the sample sizes are small. We created a set of U statistics and compared them with some existing statistics in testing homogeneity of OR at different data settings. We evaluated their performance in terms of the empirical size and power via Monto Carlo simulations. Our results showed that two of the U-statistics under our study had higher power for testing homogeneity of odds ratios for 2 by 2 contingency tables. The application of the tests was illustrated in two real examples.展开更多
基金Supported by National Natural Science Foundation of China（60774010 10971256） Natural Science Foundation of Jiangsu Province（BK2009083）+1 种基金 Program for Fundamental Research of Natural Sciences in Universities of Jiangsu Province（07KJB510114） Shandong Provincial Natural Science Foundation of China（ZR2009GM008 ZR2009AL014）
基金Funded by Fundamental Research Funds for the Central Universities (Grant No.BUPT2012RC0708)
文摘The sign of an association measure between two varibles may be strongly affected and even be reversed after marginalization over a backgruoud variable, which is the well-known Yule-Simpson paradox.Odds ratios are strongly collapsible over a background variable if they remain unchanged no matter how the background variable is partially pooled.In this paper, we firstly give some definitions and notations about odds ratios between a dichotomous explanatory variable and a continuous response variable.Then, we present conditions for simple collapsibility of odds ratios.Further, necessary and sufficient conditions are given for strong collapsibility of odds ratios for continuous outcome variable.
文摘The purpose of this paper is to bring awareness to the general public that certain conditions that occur at a buoy in the Atlantic Basin, such as wind located at the buoy, pressure located at a buoy, water temperature located at a buoy, atmospheric pressure located at a buoy, may be useful in helping predict when a hurricane could possibly hit the state of Florida in the future. One of the goals of this paper is to bring new statistical methods to investigate and analyze data, which will create better predicable measures in determining when a hurricane will possibly hit the state of Florida. In this paper, the topics of binary logistic regression and multinomial regression modeling are discussed in reference to their outcomes of both the odds ratio and relative risk ratio respectively. The coefficients from these models will show which prospective buoy conditions are possibly more responsible for indication of a storm being present in the Atlantic Basin. In this paper, the data that was used and compiled into a larger data set came from two different sources. First, the hurricane data for the years 1992-2013 came from Unisys Weather site (Atlantic Basin Hurricanes data) and the buoy data has been available from the National Buoy Center. In this paper, the variables of interest are: storm present, buoy wind speed, buoy pressure, buoy atmospheric temperature, buoy water temperature and buoy wind direction. The buoy conditions are the buoy wind, the buoy wind direction, the buoy pressure, buoy atmospheric temperature and the buoy water temperature.
文摘There are a few statistics testing the homogeneity of odds ratios across strata. Asymptotic statistics lose their power in the “sparse-data” setting. Both asymptotic statistics and exact tests have low power when the sample sizes are small. We created a set of U statistics and compared them with some existing statistics in testing homogeneity of OR at different data settings. We evaluated their performance in terms of the empirical size and power via Monto Carlo simulations. Our results showed that two of the U-statistics under our study had higher power for testing homogeneity of odds ratios for 2 by 2 contingency tables. The application of the tests was illustrated in two real examples.
