Nonparametric Instrumental Variable Estimation of Structural Quantile Effects

We study the asymptotic distribution of Tikhonov regularized estimation of quantile structural effects implied by a nonseparable model. The nonparametric instrumental variable estimator is based on a minimum distance principle. We show that the minimum distance problem without regularization is locally ill‐posed, and we consider penalization by the norms of the parameter and its derivatives. We derive pointwise asymptotic normality and develop a consistent estimator of the asymptotic variance. We study the small sample properties via simulation results and provide an empirical illustration of estimation of nonlinear pricing curves for telecommunications services in the United States.     

中国证券市场跳跃行为非参数方法

资产价格跳跃行为是连续时间金融学研究的重要问题之一。在Ait-Sahalia成功将跳跃研究视野由狭义事件驱动有限活跃跳跃扩展到广义订单驱动无限活跃跳跃层次的基础上,对阈值多幂次变差模型(TMPV)广义跳跃系列问题研究中存在的阈值时变性问题进行了改进,对其尚未讨论的广义跳跃行为属性存在的采样频率依赖性问题进行了实证研究,在此基础上对不同超高频率下的广义跳跃行为的属性特征进行了刻画,进一步分析不同类型股票的跳跃行为属性特征,结合实证结果对其原因进行了初步分析。     

基于计算一实验综合模型的啮合齿面关键摩擦特性参数反求研究

在齿轮传动动测实验的基础上,引入有限单元法多场耦合分析和反求技术,提出了面向工程的复杂齿轮传动摩擦多性态概念,建立了复杂齿轮传动的“计算一实验综合模型”：利用该综合模型对不同润滑状况下齿面摩擦因数进行了合理的反求,解决了复杂曲面摩擦因数难于求解的问题。     

Ascent-based Monte Carlo expectation– maximization

Summary.  The expectation–maximization (EM) algorithm is a popular tool for maximizing likelihood functions in the presence of missing data. Unfortunately, EM often requires the evaluation of analytically intractable and high dimensional integrals. The Monte Carlo EM (MCEM) algorithm is the natural extension of EM that employs Monte Carlo methods to estimate the relevant integrals. Typically, a very large Monte Carlo sample size is required to estimate these integrals within an acceptable tolerance when the algorithm is near convergence. Even if this sample size were known at the onset of implementation of MCEM, its use throughout all iterations is wasteful, especially when accurate starting values are not available. We propose a data-driven strategy for controlling Monte Carlo resources in MCEM. The algorithm proposed improves on similar existing methods by recovering EM's ascent (i.e. likelihood increasing) property with high probability, being more robust to the effect of user-defined inputs and handling classical Monte Carlo and Markov chain Monte Carlo methods within a common framework. Because of the first of these properties we refer to the algorithm as 'ascent-based MCEM'. We apply ascent-based MCEM to a variety of examples, including one where it is used to accelerate the convergence of deterministic EM dramatically.     

不等群下的整群抽样问题研究

在社会经济领域中,群大小不等是常见的现象。在群大小不等条件下进行整群抽样最主要的问题是样本群大小的随机变动。样本群大小的随机变动会给抽样调查带来种种不利的影响。要提高整群抽样估计的准确度,就必须控制样本群大小的变动。在群大小不等条件下,简单随机抽样无偏估计方法是不可取的,应尽可能地采用麦多方法、PPS方法或简单随机抽样比率估计方法,以提高整群抽样效率。另外,改进总体推估方式也是提高整群抽样效率的简便方法,应值得高度重视。     

一种基于同步抽样技术的周期信号高精度DFT分析

提出了一种提高周期信号ＤＦＴ分析精度的同步抽样技术，对其原理作了详细的分析，给出了实现电路。文中以一组周期信号的ＤＦＴ分析为例，比较了信号谱的幅度和相位误差，其结果表明频谱精度大大提高。     

Simulation-based designs for accelerated life tests

In this paper we present a Bayesian decision theoretic approach to the design of accelerated life tests (ALT). We discuss computational issues regarding the evaluation of expectation and optimization steps in the solution of the decision problem. We illustrate how Monte Carlo methods can be used in preposterior analysis to find optimal designs and how the required computational effort can be avoided by using curve-fitting techniques. In so doing, we adopt the recent Monte-Carlo-based approaches of Muller and Parmigiani (1995. J. Amer. Statist. Assoc. 90, 503–510) and Muller (2000. Bayesian Statistics 6, forthcoming) to develop optimal Bayesian designs. These approaches facilitate the preposterior analysis by replacing it with a sequence of scatter plot smoothing/regression techniques and optimization of the corresponding fitted surfaces. We present our development by considering single and multiple-point fixed, as well as, sequential design problems when the underlying life model is exponential, and illustrate the implementation of our approach with some examples.     

多元抽样技术（Ⅱ）

本文了在实际中已有应用的对称等距抽样方法，我们从理论上证明了这一方法的合理性。并且建立了多元有限元体的回归估计法。     

Calibrated imputation in surveys under a quasi-model-assisted approach

Summary.  We propose to use calibrated imputation to compensate for missing values. This technique consists of finding final imputed values that are as close as possible to preliminary imputed values and are calibrated to satisfy constraints. Preliminary imputed values, potentially justified by an imputation model, are obtained through deterministic single imputation. Using appropriate constraints, the resulting imputed estimator is asymptotically unbiased for estimation of linear population parameters such as domain totals. A quasi-model-assisted approach is considered in the sense that inferences do not depend on the validity of an imputation model and are made with respect to the sampling design and a non-response model. An imputation model may still be used to generate imputed values and thus to improve the efficiency of the imputed estimator. This approach has the characteristic of handling naturally the situation where more than one imputation method is used owing to missing values in the variables that are used to obtain imputed values. We use the Taylor linearization technique to obtain a variance estimator under a general non-response model. For the logistic non-response model, we show that ignoring the effect of estimating the non-response model parameters leads to overestimating the variance of the imputed estimator. In practice, the overestimation is expected to be moderate or even negligible, as shown in a simulation study.     

基于相关系数的ISAR干扰效果评估方法

从二维相关系数的角度出发,提出了一种对ISAR压制干扰效果的评估方法。该方法能清楚地反映受干扰目标从临界干扰到严重干扰整个干扰过程中目标ISAR图像失真程度;并将干扰效果评估量化在0～1之间,具有归一化的优点。就射频噪声干扰模式和调相噪声干扰模式对ISAR进行了干扰仿真,仿真结果证明了该评估方法的有效性。