Will Swedish and Dutch drug policy converge? The role of theory

Sweden and The Netherlands are often referred to as the archetypes of prohibition and harm reduction, respectively. Both nations have, however, recently adopted political measures that fit well in the other country's model. But they have also taken steps that reflect traditional thinking. Are we witnessing the initial stages of convergence or are recent developments better interpreted as adjustments? This article addresses these questions by comparing Swedish and Dutch theoretical understandings of ‘the drug problem’. Goals and practices are illuminated in the context of underlying theory, raising the question: Can substantial convergence be achieved without significant prior changes in theoretical perceptions? It is found that the theoretical understandings of the two paradigms do not mix easily, complicating attempts at convergence. Moreover, both countries have invested a great deal of political prestige in their respective models, further exacerbating the problem. A conceivable way to circumvent these difficulties is suggested.     

A regression-based smoothing spline Monte Carlo algorithm for pricing American options in discrete time

American options in discrete time can be priced by solving optimal stopping problems. This can be done by computing so-called continuation values, which we represent as regression functions defined recursively by using the continuation values of the next time step. We use Monte Carlo to generate data, and then we apply smoothing spline regression estimates to estimate the continuation values from these data. All parameters of the estimate are chosen data dependent. We present results concerning consistency and the estimates’ rate of convergence.     

On Rates of Convergence for Bayesian Density Estimation

Abstract.  We consider the problem of estimating a compactly supported density taking a Bayesian nonparametric approach. We define a Dirichlet mixture prior that, while selecting piecewise constant densities, has full support on the Hellinger metric space of all commonly dominated probability measures on a known bounded interval. We derive pointwise rates of convergence for the posterior expected density by studying the speed at which the posterior mass accumulates on shrinking Hellinger neighbourhoods of the sampling density. If the data are sampled from a strictly positive, α -Hölderian density, with α  ∈ ( 0,1] , then the optimal convergence rate n^{− α / (2 α +1)} is obtained up to a logarithmic factor. Smoothing histograms by polygons, a continuous piecewise linear estimator is obtained that for twice continuously differentiable, strictly positive densities satisfying boundary conditions attains a rate comparable up to a logarithmic factor to the convergence rate n ^−4/5 for integrated mean squared error of kernel type density estimators.     

Optimal scaling of Metropolis algorithms: Heading toward general target distributions

The authors provide an overview of optimal scaling results for the Metropolis algorithm with Gaussian proposal distribution. They address in more depth the case of high‐dimensional target distributions formed of independent, but not identically distributed components. They attempt to give an intuitive explanation as to why the well‐known optimal acceptance rate of 0.234 is not always suitable. They show how to find the asymptotically optimal acceptance rate when needed, and they explain why it is sometimes necessary to turn to inhomogeneous proposal distributions. Their results are illustrated with a simple example.     

块Jacobi迭代阵的收敛性

研究大型线性方程组迭代解法中分块ＪＡＣＯＢＩ迭代阵的收敛性。采用块矩阵分析方法和谱半径降维估计法得到块Ｊａｃｏｂｉ迭代阵收敛的实用充分条件。     

区间数互补判断矩阵的一致性及其排序研究

由于目前国内外文献对区间数互补判断矩阵的性质研究较少,从而使得对排序方法的相关研究缺乏理论依据.针对这些缺陷,本文研究了区间数互补判断矩阵的性质及其排序方法问题.根据区间数互补判断矩阵的定义,给出了区间数互补判断矩阵的一致性、严格强传递性与弱传递性等定义,并研究了一致性判断矩阵的性质,并说明这些性质更符合人们的思维特征.在一致性性质的基础上建立了区间数互补判断矩阵排序的非线性规划模型,算例分析表明该方法是有效可行的.     

经济收敛理论与检验方法研究综述

在大量文献研究的基础上,对收敛问题的产生及收敛概念的发展作了全面概括,并对绝对收敛、条件收敛、俱乐部收敛和σ收敛作了深入辨析,对各种识别方法作了全面归纳。归纳了对世界大多数国家和地区数据所作识别的结果。还从收敛理论与传统理论的冲突与协调方面作了深入分析。最后对近年收敛研究的深化与扩展研究的趋势作了概括。     

Complete moment convergence and Lr convergence for asymptotically almost negatively associated random variables

In this paper, we investigate the complete moment convergence and L_r convergence for maximal partial sums of asymptotically almost negatively associated random variables under some general conditions. The results obtained in the paper generalize some corresponding ones for negatively associated random variables.     

The almost sure convergence rate of the estimator of optimized certainty equivalent risk measure under α-mixing sequences

Conditional value-at-risk (CVaR) model is a kind of financial risk measure that is extensively supported and accepted by international financial community. Its optimized form can be regarded as an optimized certainty equivalent (OCE) risk measurement. In this paper, we mainly discuss and analyze the strong laws of large numbers and the convergence rate of OCE's estimator under α-mixing sequences. The result shows that the almost sure convergence rate of CVaR estimator is given by the results of OCE estimator. Its convergence rate is inversely proportional to the square root of the sample size under certain conditions. Its effectiveness is verified by simulation experiments for two classical α-mixing sequences.     

Empirical Bayes estimation in continuous one-parameter exponential families under associated samples

In this paper, we study the empirical Bayes (EB) estimation in continuous one-parameter exponential families under negatively associated (NA) samples and positively associated (PA) samples. Under certain regularity conditions, it is shown that the convergence rates of proposed EB estimators under NA or PA samples are the same as those of EB estimators under independent observations, which significantly improve the existing results in EB estimation under associated samples.