A GEE approach to estimating accuracy and its confidence intervals for correlated data

In this paper, we provide a method for constructing confidence interval for accuracy in correlated observations, where one sample of patients is being rated by two or more diagnostic tests. Confidence intervals for other measures of diagnostic tests, such as sensitivity, specificity, positive predictive value, and negative predictive value, have already been developed for clustered or correlated observations using the generalized estimating equations (GEE) method. Here, we use the GEE and delta‐method to construct confidence intervals for accuracy, the proportion of patients who are correctly classified. Simulation results verify that the estimated confidence intervals exhibit consistent/appropriate coverage rates.     

关于ord_a(b~n±1)的上界

设ａ ,ｂ 是互素的正整数,ａ ＞ １,ｂ＞ １ ．本文给出了ｏｒｄａ（ ｂｎ ±１） 的最好的上界：设ν是使得ａα｜ｂν±１ 　及　ｇｃｄ（ａ ,ｂυ±１ａα） ＝１成立的最小正整数,而α≥２ 是一个整数．令ｃａ ,ｂ ＝ ａα／υ,则对于任何正整数ｎ,都有ｏｒｄａ（ｂｎ±１） ≤ｌｏｇ（ Ｃａ ,ｂｎ）ｌｏｇａ ．     

The Effect of Treatment Compliance in a Placebo-controlled Trial: Regression with Unpaired Data

The causal effect of a treatment is estimated at different levels of treatment compliance, in a placebo-controlled trial on the reduction of blood pressure. The structural nested mean model makes no direct assumptions on selected treatment compliance levels and placebo prognosis but relies on the randomization assumption and a parametric form for causal effects. It can be seen as a regression model for unpaired data, where pre- and post-randomization covariables are treated differently. The causal parameters are found as solutions to estimating equations involving estimated placebo response and treatment compliance based on base-line covariates for all subjects. Our example considers a linear effect of the percentage of prescribed dose taken on achieved diastolic blood pressure reduction. We propose an exploration of structural model checks. In the example, this reveals an interaction between the causal effect of active dose taken and the base-line body weight of the patient.     

Persistence and existence of stationary measures for a logistic growth model with predation

We consider a stochastic logistic growth model with a predation term, and a diffusive stochastic part with a power-type coefficient. We provide criteria for the persistence of the population and for the existence and uniqueness of a stationary measure. Furthermore, we perform a detailed study of the densities of the stationary measures resorting to the forward Kolmogorov equation. We compile our results in a stochastic bifurcation diagram, drawing comparisons with the corresponding deterministic model.     

具有正负项系数的一阶中立型时滞微分方程的振动性

利用时滞微分方程与时滞微分不等式之间的一种等价关系,得到了具有正负项系数的一阶中立型时滞微分方程：d/dt[x（t）-C（t）x（t-r）]＋P（t）x（t-τ）-Q（t）x（t-δ）=0一切解振动的充分条件。结果推广了当Q（t）≡q〉0时的主要结论。     

An IV Model of Quantile Treatment Effects

The ability of quantile regression models to characterize the heterogeneous impact of variables on different points of an outcome distribution makes them appealing in many economic applications. However, in observational studies, the variables of interest (e.g., education, prices) are often endogenous, making conventional quantile regression inconsistent and hence inappropriate for recovering the causal effects of these variables on the quantiles of economic outcomes. In order to address this problem, we develop a model of quantile treatment effects (QTE) in the presence of endogeneity and obtain conditions for identification of the QTE without functional form assumptions. The principal feature of the model is the imposition of conditions that restrict the evolution of ranks across treatment states. This feature allows us to overcome the endogeneity problem and recover the true QTE through the use of instrumental variables. The proposed model can also be equivalently viewed as a structural simultaneous equation model with nonadditive errors, where QTE can be interpreted as the structural quantile effects (SQE).     

GM模型的病态性问题

本文指出GM模型的方程组存在着严重的方程病态性问题,探讨了GM模型产生病态的原因:(1)矩阵由累加数构成,累加数之间有较大的数量级差别;(2)GM模型中的蜕化使矩阵中元素数量级差别过大;(3)GM模型的矩阵生成方式使得我们难以降低方程组的病态性。并对一个被广泛引用的算例进行了分析。最后,我们认为必须小心使用GM模型。     

Periodic Solutions of Linear Neutral Differential Equations

研究了具有无穷时滞的线性中立型方程与高阶中立型方程的周期解问题。利用Ｆｏｕｒｉｅｒ指数级数理论,获得了线性中立型方程存在周期解的充分必要条件。文中用例子说明了结果的实用性。     

麦克斯韦方程组研讨

提出了另一种说理,导证麦克斯韦方程组方法,期望在系统性、简易性方面有所改进。