Bias optimality for multichain continuous-time Markov decision processes

This paper deals with the bias optimality of multichain models for finite continuous-time Markov decision processes. Based on new performance difference formulas developed here, we prove the convergence of a so-called bias-optimal policy iteration algorithm, which can be used to obtain bias-optimal policies in a finite number of iterations.     

Rolling-ball method for estimating the boundary of the support of a point-process intensity

We suggest a generalisation of the convex-hull method, or ‘DEA’ approach, for estimating the boundary or frontier of the support of a point cloud. Figuratively, our method involves rolling a ball around the cloud, and using the equilibrium positions of the ball to define an estimator of the envelope of the point cloud. Constructively, we use these ideas to remove lines from a triangulation of the points, and thereby compute a generalised form of a convex hull. The radius of the ball acts as a smoothing parameter, with the convex-hull estimator being obtained by taking the radius to be infinite. Unlike the convex-hull approach, however, our method applies to quite general frontiers, which may be neither convex nor concave. It brings to these contexts the attractive features of the convex hull: simplicity of concept, rotation-invariance, and ready extension to higher dimensions. It admits bias corrections, which we describe and illustrate through implementation.     

Stein–type shrinkage quantile estimation

The simultaneous asymptotic estimation theory of quantiles is considered for an arbitrary population. The Stein–type estimator and its positive version are considered. The relative merits of the proposed estimators are compared with those of the usual estimator using asymptotic quadratic distributional risk those of the usual estimator using asymptotic quadratic distributional risk under local alternatives. It is shown that both proposed estimators are asymptotically superior to the classical estimator. Further, it is demonstrated that the Stein-type estimator is dominated by its positive part     

Bias phenomenon and compensation for PDA/JPDA algorithms

Probabilistic Data Association (PDA) and Joint PDA (JPDA) algorithms are approaches for target tracking which have received considerable attention. It has been observed for some years that they both yield biased tracks in a multitarget environment. However, most work assumes no false alarms and the rejection phenomenon of the JPDA algorithm has not been reported. In this paper, the general procedure of multitarget tracking and the PDA/JPDA algorithms are first described. Their bias phenomenon is simulated and investigated. It is observed that

(1) the JPDA algorithm has less bias than the PDA algorithm in a clean environment. Both of them yield coalescence

(2) the JPDA algorithm has coalescence and rejection bias phenomenon while the PDA algorithm has only coalescence phenomenon in a clutter environment.

Bias compensated algorithms are then presented using the polynomial regression method. Simulations are carried out to select the order of polynomial regression. Monte Carlo simulations also demonstrate the effectiveness of the compensated PDA/JPDA algorithms.     

汞溴红敏化TiO2纳米多孔膜电极的光电化学性质研究

用汞溴红(Mercurochrome)作敏化剂敏化TiO2纳米多孔膜电极,UV-Vis,光电流作用谱和SPS谱表明,该敏化剂能有效地敏化TiO2电极,并且染料聚集体能扩大染料的敏化区间,增加了太阳光的利用效率,对电极施加不同的偏压,测试电极产生的瞬态光电流,研究了偏压对光生电荷转移及复合速率的影响,从不同方向照射电极,对阳极光电流影响显著,结合电化学、光电化学原理对这些现象进行了讨论.     

求收益模糊的贝叶斯纳什均衡解的结构元方法

主要目的是利用结构元方法求解收益模糊的贝叶斯纳什均衡.首先,在原有结构元理论基础上,给出了多元模糊值函数的定义及其结构元表示;其次,给出了在混合策略下,收益模糊的贝叶斯纳什均衡的定义,并证明了其存在性定理;然后,利用结构元理论,将该博弈模型等价地转化为一个经典的博弈模型,简化了原问题的求解.最后的应用实例说明了该方法的有效性.     

Reducing asymptotic bias of weak instrumental estimation using independently repeated cross-sectional information

In this paper, we consider the instrumental variable estimation (the two-stage least squares estimator and the limited information maximum likelihood estimator) using weak instruments in a repeated measurements or a panel data model. We show that independently repeated cross-sectional data can reduce the asymptotic bias of the instrumental variable estimation when instruments are weakly correlated with endogenous variables. When the number of repeated measurements tends to infinity, we can achieve consistent instrumental variable estimation with weak instruments.     

Multivariate density estimation using dimension reducing information and tail flattening transformations

We propose a nonparametric multiplicative bias corrected transformation estimator designed for heavy tailed data. The multiplicative correction is based on prior knowledge and has a dimension reducing effect at the same time as the original dimension of the estimation problem is retained. Adding a tail flattening transformation improves the estimation significantly-particularly in the tail-and provides significant graphical advantages by allowing the density estimation to be visualized in a simple way. The combined method is demonstrated on a fire insurance data set and in a data-driven simulation study.     

SnO_(2)/ITO光电化学电极制备及性能研究EI北大核心CSCD

采用商品化氧化铟锡(ITO)导电玻璃、纳米SnO_(2)水胶体分散液,通过旋涂法制备了高性能SnO_(2)/ITO电极,考察了SnO_(2)旋涂浓度、旋涂量、Triton X-100浓度、煅烧温度对SnO_(2)/ITO电极性能的影响,并优化了缓冲体系、pH、浓度及偏压条件。结果表明,当SnO_(2)水胶体分散液浓度15%、Triton X-100浓度0.01%、煅烧温度450℃时,制备的SnO_(2)/ITO电极具有较高的热稳定性和光电化学性能;草酸-草酸钠缓冲体系有利于光电流的产生,当草酸浓度为30 mmol/L(pH 6.0)、偏压为0.5 V时,可以产生较高的稳态光电流信号,以Ru(bpy)_(2)(dppz)^(2+)为光电化学信号探针,检测限为10 nmol/L。考察了ITO电极的性能差异性,同一批次及不同批次之间的光电流信号一致性相对标准偏差(RSD)在3.9%~5.6%之间。     

Internal standards: a source of analytical bias for volatile organic analyte determinations

The use of internal standards in the determination of volatile organic compounds as described in SW-846 Method 8260C introduces a potential for bias in results once the internal standards (ISTDs) are added to a sample for analysis. The bias is relative to the dissimilarity between the analyte and internal standard physical properties that influence how easily analytes are separated from a matrix and concentrated during analysis. Method 5032 is a vacuum distillation procedure for extracting analytes from a sample for use with Method 8260C. Vacuum distillation is also incorporated within another GC/MS analytical procedure, Method 8261A. Method 8260C/5032 and Method 8261A are experimentally identical, however, Method 8261A uses internal standards differently by relating the recovery of each compound to its boiling point and relative volatility. By processing each analysis (water, soil, and biota) using both Method 8260C and Method 8261A, the two approaches are compared on the basis of analyte bias and the failure rate of the quality controls. Analytes were grouped by how similar their boiling points and natural log of their relative volatilities (lnRVs) were to their Method 8260C recommended ISTDs. For the most similar analytes, the Method 8260C determinations yielded an average bias less than 10% and a failure to meet calibration criteria less than 7%. However, as the difference between analyte and ISTD became greater the bias increased to over 40% (matrix dependent) and its calibration failure rate approached 70%. In comparison, when the Method 8260C data were reprocessed as Method 8261A determinations, this trend for groupings was minimized with biases increasing from 6% to only 20% and the calibration failure rate went from 0% to 15%.