When it comes to evaluating the effectiveness of interventions, the random experiment is considered the ＂gold standard＂. Randomization is considered the gold standard because it provides a way of decreasing the chan...When it comes to evaluating the effectiveness of interventions, the random experiment is considered the ＂gold standard＂. Randomization is considered the gold standard because it provides a way of decreasing the chance that systematic differences, other than type of intervention, will be obtained between treatment and control groups. What has received little attention in the literature, however, is the fact that even with random assignment researchers may end up facing problems similar to those faced with data from a study that did not use randomization. This is because attrition may result in the values of potentially confounding variables no longer being ＂balanced＂ between （or among） the groups under investigation. This means that in order to estimate the effect of the treatment, one must find some way of adjusting for these potential confounders. Although multiple regression modeling is the way social science researchers typically control for the effects of potentially confounding variables, this paper argues that a modification of multiple regression modeling that uses propensity scores, under some conditions, may provide more parsimonious and better fitting models.展开更多
In this paper the influence of the differently distributed phase-randontized to the data obtained in dynamic analysis for critical value is studied.The calculation results validate that the sufficient phase-randomized...In this paper the influence of the differently distributed phase-randontized to the data obtained in dynamic analysis for critical value is studied.The calculation results validate that the sufficient phase-randomized of the different distributed random numbers are less influential on the critical value . This offers the theoretical foundation of the feasibility and practicality of the phase-randomized method.展开更多
The famous de Moivre’s Laplace limit theorem proved the probability density function of Gaussian distribution from binomial probability mass function under specified conditions. De Moivre’s Laplace approach is cumbe...The famous de Moivre’s Laplace limit theorem proved the probability density function of Gaussian distribution from binomial probability mass function under specified conditions. De Moivre’s Laplace approach is cumbersome as it relies heavily on many lemmas and theorems. This paper invented an alternative and less rigorous method of deriving Gaussian distribution from basic random experiment conditional on some assumptions.展开更多
文摘When it comes to evaluating the effectiveness of interventions, the random experiment is considered the ＂gold standard＂. Randomization is considered the gold standard because it provides a way of decreasing the chance that systematic differences, other than type of intervention, will be obtained between treatment and control groups. What has received little attention in the literature, however, is the fact that even with random assignment researchers may end up facing problems similar to those faced with data from a study that did not use randomization. This is because attrition may result in the values of potentially confounding variables no longer being ＂balanced＂ between （or among） the groups under investigation. This means that in order to estimate the effect of the treatment, one must find some way of adjusting for these potential confounders. Although multiple regression modeling is the way social science researchers typically control for the effects of potentially confounding variables, this paper argues that a modification of multiple regression modeling that uses propensity scores, under some conditions, may provide more parsimonious and better fitting models.
文摘In this paper the influence of the differently distributed phase-randontized to the data obtained in dynamic analysis for critical value is studied.The calculation results validate that the sufficient phase-randomized of the different distributed random numbers are less influential on the critical value . This offers the theoretical foundation of the feasibility and practicality of the phase-randomized method.
文摘The famous de Moivre’s Laplace limit theorem proved the probability density function of Gaussian distribution from binomial probability mass function under specified conditions. De Moivre’s Laplace approach is cumbersome as it relies heavily on many lemmas and theorems. This paper invented an alternative and less rigorous method of deriving Gaussian distribution from basic random experiment conditional on some assumptions.
