共查询到18条相似文献,搜索用时 125 毫秒
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本文基于因果推断理论,提出根据病人的生物标记物进行最优治疗方案选择的统计方法.这种方法是基于CATE (conditional average treatment effect)曲线以及CATE曲线的置信带(SCB)的. CSTE曲线表示给定生物标记物(协变量)的条件下,处理组的条件平均处理效应.同时, CATE曲线及其SCB可以被用于对特定的治疗方案选择适宜的病人.文中利用B样条方法估计CATE曲线及其CSB,并推导了其近似大样本性质.文中还通过模拟比较研究了CATE曲线的置信带的有限样本性质,并阐述了CATE曲线及其置信带在真实数据中如何选择最优治疗方案. 相似文献
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本文研究测量误差模型的自适应LASSO(least absolute shrinkage and selection operator)变量选择和系数估计问题.首先分别给出协变量有测量误差时的线性模型和部分线性模型自适应LASSO参数估计量,在一些正则条件下研究估计量的渐近性质,并且证明选择合适的调整参数,自适应LASSO参数估计量具有oracle性质.其次讨论估计的实现算法及惩罚参数和光滑参数的选择问题.最后通过模拟和一个实际数据分析研究了自适应LASSO变量选择方法的表现,结果表明,变量选择和参数估计效果良好. 相似文献
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基于稀疏group lasso的思想和adaptive lasso的优点,提出更具一般性的Lp正则化的自适应稀疏group lasso,并对其高维统计性质进行了研究.通过对正则子、损失函数的性质和正则参数的选择的分析,最终得到基于Lp正则化的自适应稀疏group lasso非渐近误差界估计. 相似文献
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在本文中,我们着重研究了极值指数的修正的Pickands型估计的样本点分割方法.我们在渐近二阶矩最小的准则下,利用子样本自助法给出了修正的Pickands型估计的样本点分割方法,从理论上证明了该估计的大样本性质,说明了这种分割在渐近二阶矩最小的准则下是渐近最优分割,同时提出了自适应的样本点分割的自助算法. 相似文献
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该文提出了一种一步估计方法用以估计变系数模型中具有互不相同光滑度的未知函数, 所有未知函数和它们的导数的估计量由 一次极小化得到. 给出了估计量的渐近性质, 包括渐近偏差、方差和渐近分布, 一步估计量被证明达到了最优收敛速度. 相似文献
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给出了一种用于估计变系数模型中未知函数的逐元B-Spline方法,建立了估计量的局部渐近偏差,方差和渐近正态分布,开发了一种快速选择估计量窗宽的方法,通过Monte Carlo模拟研究了估计量的有限样本性质. 相似文献
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TANG Qingguo WANG Jinde Institute of Sciences PLA University of Science Technology Nanjing China 《数学年刊A辑(中文版)》2007,(5)
给出了一种用于估计变系数模型中未知函数的逐元B-Spline方法,建立了估计量的局部渐近偏差,方差和渐近正态分布,开发了一种快速选择估计量窗宽的方法,通过Monte Carlo模拟研究了估计量的有限样本性质. 相似文献
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A class of optimal adaptive multi-arm clinical trial designs is proposed based on an extended generalized Pólya urn (GPU) model. The design is applicable to both the qualitative and quantitative responses and achieves, asymptotically, some pre-specified optimality criterion. Such criterion is specified by a functional of the response distributions and is implemented through the relationship between the design matrix and its first eigenvector. The asymptotic properties of the design are studied using the existing methods on GPU. Some examples for commonly used clinical designs are given as illustration. 相似文献
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In this paper,for time-to-event data,we propose a new statistical framework for casual inference in evaluating clinical utility of predictive biomarkers and in selecting an optimal treatment for a particular patient.This new casual framework is based on a new concept,called Biomarker Adjusted Treatment Effect (BATE) curve.The BATE curve can be used for assessing clinical utility of a predictive biomarker,for designing a subsequent confirmation trial,and for guiding clinical practice.We then propose semi-parametric methods for estimating the BATE curves of biomarkers and establish asymptotic results of the proposed estimators for the BATE curves.We also conduct extensive simulation studies to evaluate finite-sample properties of the proposed estimation methods.Finally,we illustrate the application of the proposed method in a real-world data set. 相似文献
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We consider the constrained optimization of a finite-state, finite action Markov chain. In the adaptive problem, the transition probabilities are assumed to be unknown, and no prior distribution on their values is given. We consider constrained optimization problems in terms of several cost criteria which are asymptotic in nature. For these criteria we show that it is possible to achieve the same optimal cost as in the non-adaptive case.We first formulate a constrained optimization problem under each of the cost criteria and establish the existence of optimal stationary policies.Since the adaptive problem is inherently non-stationary, we suggest a class ofAsymptotically Stationary (AS) policies, and show that, under each of the cost criteria, the costs of an AS policy depend only on its limiting behavior. This property implies that there exist optimal AS policies. A method for generating adaptive policies is then suggested, which leads to strongly consistent estimators for the unknown transition probabilities. A way to guarantee that these policies are also optimal is to couple them with the adaptive algorithm of [3]. This leads to optimal policies for each of the adaptive constrained optimization problems under discussion.This work was supported in part through United States-Israel Binational Science Foundation Grant BSF 85-00306. 相似文献
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Zhang Li-Xin 《Journal of multivariate analysis》2006,97(3):586-605
The play-the-winner (PW) rule is an important method in clinical trials where patients can be assigned to one of the two treatments. In the PW rule, the probability of the next patient to be assigned to a particular treatment only depends on the response of the current patient. In this paper, we consider a general kind of PW rule for multi-treatment adaptive designs, in which the probability that a treatment is assigned to the next patient depends upon both the response of the previous patient and an estimated parameter, e.g., the observed success rate. Using this kind of adaptive designs, more information of previous stages are used to update the model at each stage, and more patients may be assigned to better treatments. The strong consistency and the asymptotic normality are established for the allocation proportions. 相似文献
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P. Florchinger 《Applied Mathematics and Optimization》1998,38(1):109-120
The purpose of this paper is to study the problem of asymptotic stabilization in probability of nonlinear stochastic differential
systems with unknown parameters. With this aim, we introduce the concept of an adaptive control Lyapunov function for stochastic
systems and we use the stochastic version of Artstein's theorem to design an adaptive stabilizer. In this framework the problem
of adaptive stabilization of a nonlinear stochastic system is reduced to the problem of asymptotic stabilization in probability
of a modified system. The design of an adaptive control Lyapunov function is illustrated by the example of adaptively quadratically
stabilizable in probability stochastic differential systems.
Accepted 9 December 1996 相似文献
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Adaptive approximation (or interpolation) takes into account local variations in the behavior of the given function, adjusts
the approximant depending on it, and hence yields the smaller error of approximation. The question of constructing optimal
approximating spline for each function proved to be very hard. In fact, no polynomial time algorithm of adaptive spline approximation can be designed and no exact
formula for the optimal error of approximation can be given. Therefore, the next natural question would be to study the asymptotic
behavior of the error and construct asymptotically optimal sequences of partitions. In this paper we provide sharp asymptotic
estimates for the error of interpolation by splines on block partitions in
\mathbbRd{\mathbb{R}^d} . We consider various projection operators to define the interpolant and provide the analysis of the exact constant in the
asymptotics as well as its explicit form in certain cases. 相似文献
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The article deals with an optimal response-adaptive design for allocating patients among two competing treatments in a phase III clinical trial. An optimal response-adaptive target is developed for a general class of response distributions subject to two clinically relevant constraints. Various theoretical and numerical properties of the proposed procedure are investigated and compared with some existing competitors. A data study is also included for the assessment of the proposed procedure in real situation. 相似文献
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《Optimization》2012,61(3):397-414
In this article we study the hybrid extragradient method coupled with approximation and penalty schemes for convex minimization problems. Under certain hypotheses, which include, for example, the case of Tikhonov regularization, we prove asymptotic convergence of the method to the solution set of our minimization problem. When we use schemes of penalization or barrier, we can show asymptotic convergence using the well-known fast/slow parameterization techniques and exploiting the existence and finite length of an optimal path. 相似文献