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存在不依从与对照组接受治疗时比例风险模型中的治疗效应估计AI 解读

Summary Methods for adjusting for non-compliance and contamination, which respect the randomization, are extended from binary outcomes to time-to-event analyses by using a proportional hazards model. A simple non-iterative method is developed when there are no covariates, which is a generalization of the Mantel–Haenszel estimator. More generally, a ‘partial likelihood’ is developed which accommodates covariates under the assumption that they are independent of compliance. A…

半参数加性回归AI 解读

SUMMARY A simple estimator for β is proposed for the model y = x'β + g(t)+ error, g smooth but unknown. The approach is to approximate the estimating equation obtained from a ***semiparametric likelihood and in the simplest case reduces to minimizing the distance between the 'pseudoresiduals' y - x'β and a local linear cross-validated estimate of them. When the errors are independent with finite variance, the bias and variance of the estimate are computed and compared…

未知误差分布下半参数加性回归模型的有效估计AI 解读

Several authors have shown how to efficiently estimate $\beta$ in the semiparametric additive model $y = x'\beta + g(t) + \text{error}$, $g(t)$ smooth but unknown when the error distribution is normal. However, the general theory suggests that efficient estimation should be possible for general error distributions with finite Fisher information even when the error distribution is unknown. In this note we construct a sequence of estimators which achieves this goal under…

非均匀人口中的空间聚集检测AI 解读

SUMMARY A new method for detecting spatial clustering of events in populations with non-uniform density is proposed. The method is based on selecting controls from the population at risk and computing interpoint distances for the combined sample. Nonparametric tests are developed which are based on the number of cases among the k nearest neighbours of each case and the number of cases nearer than the k nearest control. The performance of these tests is evaluated analytically…

秩回归AI 解读

An estimation procedure for $(b, g)$ is developed for the transformation model $g(Y) = bz + \text{error, where} g$ is an unspecified strictly increasing function. The estimator for $b$ can be viewed as a hybrid between an $M$-estimator and an $R$-estimator. It differs from an $M$-estimator in that the dependent variable is replaced by a score based on ranks and from an $R$-estimator in that the ranks of dependent variable itself are used, not the ranks of the residuals. This…

删失线性秩检验的渐近性质AI 解读

A conjecture of Prentice is established which states that for censored linear rank test, exact scores based on conditional expectations can be replaced by approximate scores obtained by evaluating the score function at an estimate of the survival function. We show that under minimal conditions, asymptotically equivalent tests are obtained when either the Kaplan-Meier, Altshuler, or moment estimator of the survival function is used. Asymptotic normality is also established…

右删失数据的关联秩检验AI 解读

Rank tests for association between a time variable and a continuous covariate, and between two time variables are given in the presence of censoring. In the first case, these tests are related to the semiparametric tests of Cox (1972) and Prentice (1978) but differ in that the covariate is replaced by a score computed from its rank. In the second case, the test statistic is again a simple inner product, which in the proportional hazards model reduces to the correlation…

利用未知水平的上穿次数估计频率AI 解读

By counting the number of upcrossings of the mean level by a stationary Gaussian process one can estimate the mean frequency of the process. Here we present a frequency estimator based on the number of upcrossings of one unknown level hoped to be near the mean, and the percentage of the total observation time spent above the level. This is equivalent to observing upcrossings of a known level in a process with unknown mean and variance. Formulae are given for the bias and…

利用未知水平的上穿越估计频率AI 解读

By counting the number of upcrossings of the mean level by a stationary Gaussian process one can estimate the mean frequency of the process. Here we present a frequency estimator based on the number of upcrossings of one unknown level hoped to be near the mean, and the percentage of the total observation time spent above the level. This is equivalent to observing upcrossings of a known level in a process with unknown mean and variance. Formulae are given for the bias and…