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中国临床医学研究发展现状与未来展望
临床医学 临床研究 竞争力分析 计量分析
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2020/9/25
近年来，中国在临床医学研究方面进行了多项规划与布局并取得了较好的成效。为系统反映中国临床医学研究发展现状，分析我国临床研究整体水平，本文梳理了全球及中国的临床医学研究论文、临床试验、技术转化等现状，分析了中国在临床医学产业链中的竞争力水平。调研结果显示，2018年中国临床医学论文数量已达全球第二，主持或参与的临床试验数量达全球第三。总体来看，中国目前尚缺乏高质量、系统性的临床医学研究体系和具有全球...
基于流形学习的非一致性判断矩阵排序方法
判断矩阵 一致性检测 排序方法
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2015/11/12
针对传统层次分析法(AHP)在构造判断矩阵过程中需要满足一致性条件问题,本文研究AHP方法需要进行一致性调整的原因,提出了一种基于流形学习的非一致性判断矩阵排序方法。在非一致性判断矩阵排序过程中,首先基于近邻距离的概念,构建出判断矩阵所对应数据集的近邻距离矩阵;然后以近邻点的线性表示为基础,将每个数据点映射到一个全局低维坐标系,并据此获得判断矩阵所对应的低维嵌入;根据各层求解出的低维嵌入对各层要素...
低碳环境下的车辆路径问题及禁忌搜索算法研究
禁忌搜索算法 能耗 环保
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2015/11/12
基于节能减排的新视角,本文研究了低碳环境下由第三方提供运输服务的车辆路径问题,在安排车辆路径时,同时考虑了能耗、碳排放和租车费用,而这些费用不仅与距离有关,也与客户点的需求量和车辆速度有关。提出了考虑车辆运量和速度的能耗计算方法,建立了非满载运输方式下的低碳路径模型——LCRP。设计了基于路径划分的禁忌搜索算法RS-TS对问题进行求解,该算法引入了一种新颖的路径编码与解码算法WSS,采用了三种邻域...
Maximum likelihood and generalized spatial two-stage least-squares estimators for a spatial-autoregressive model with spatial-autoregressive disturbances
spreg spatial-autoregressive models
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2015/9/24
We describe the spreg command, which implements a maximum
likelihood estimator and a generalized spatial two-stage least-squares estimator
for the parameters of a linear cross-sectional spatial-auto...
Creating and managing spatial-weighting matrices with the spmat command
spmat spatial-autoregressive models
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2015/9/24
We present the spmat command for creating, managing, and storing
spatial-weighting matrices, which are used to model interactions between spatial
or more generally cross-sectional units. spmat can s...
Determinant Maximization with Linear Matrix Inequality Constraints
Determinant Maximization Linear Matrix Inequality Constraints
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2015/7/10
The problem of maximizing the determinant of a matrix subject to linear matrix inequalities arises in many fields, including computational geometry, statistics, system identification, experiment desig...
Entropy and random feedback
Entropy random feedback
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2015/7/10
The gamma-entropy is a convex function of matrices that is closely related to the Frobenius and spectral (maximum singular value) norms. It comes up in several applications such as central H-infinity ...
An Ellipsoidal Approximation to the Hadamard Product of Ellipsoids
Ellipsoidal Approximation Hadamard Product Ellipsoids
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2015/7/10
This paper introduces a computationally efficient outer approximation to the Hadamard, i.e., element-wise, product of two ellipsoids. This element-wise product corresponds to multiplicative uncertaint...
Log-Det Heuristic for Matrix Rank Minimization with Applications to Hankel and Euclidean Distance Matrices
Log-Det Heuristic Matrix Rank Minimization Applications Hankel Euclidean Distance Matrices
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2015/7/10
We present a heuristic for minimizing the rank of a positive semidefinite matrix over a convex set. We use the logarithm of the determinant as a smooth approximation for rank, and locally minimize thi...
Adaptive Importance Sampling via Stochastic Convex Programming
Adaptive Importance Sampling via Stochastic Convex Programming
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2015/7/8
We show that the variance of the Monte Carlo estimator that is importance sampled from an exponential family is a convex function of the natural parameter of the distribution. With this insight, we pr...
Likelihood Ratio Gradient Estimation for Stochastic Recursions
Likelihood Ratio Gradient Estimation Stochastic Recursions
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2015/7/8
In this paper, we develop mathematical machinery for verifying that a broad class of general state space Markov chains reacts smoothly to certain types of perturbations in the underlying transition st...
Computing the Distribution Function of a Conditional Expectation via Monte Carlo: Discrete Conditioning Spaces
Probability algorithms distribution functions conditional expectation
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2015/7/8
We examine different ways of numerically computing the distribution function of conditional expectations where the conditioning element takes values in a finite or countably infinite outcome space. Bo...
Recurrence Properties of Autoregressive Processes with Super-Heavy-Tailed Innovations
Recurrence Properties Autoregressive Processes Super-Heavy-Tailed Innovations
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2015/7/6
This paper studies recurrence properties of autoregressive (AR) processes with `super-heavy-tailed' innovations. Specifically, we study the case where the innovations are distributed, roughly speaking...
Wide-sense Regeneration for Harris Recurrent Markov Processes: An Open Problem
Harris recurrence Markov chains Markov processes regeneration renewal theory
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2015/7/6
Harris recurrence is a widely used tool in the analysis of queueing systems. For discrete time Harris chains, such systems automatically exhibit wide-sense regenerative structure, so that renewal theo...
On Exponential Limit Laws for Hitting Times of Rare Sets for Harris Chains and Processes
Hitting times regenerative process Harris recurrent Markov chains Harris recurrent Markov processes
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2015/7/6
This paper provides a simple proof for the fact that the hitting time to an infrequently visited subset for a one-dependent regenerative process converges weakly to an exponential distribution. Specia...