Cross-Entropy Minimization Estimation for Two-Phase Sampling and Non-Response

This paper considers the problem of estimating the finite population total in two-phase sampling when some information on auxiliary variable is available.The authors employ an informationtheoretic approach which makes use of effective distance between the estimated probabiUties and the empirical frequencies.It is shown that the proposed cross-entropy minimization estimator is more efficient than the usual estimator and has some desirable large sample properties.With some necessary modifications,the method can be applied to two-phase sampling for stratification and non-response.A simulation study is presented to assess the finite sample performance of the proposed estimator.     

Variable Selection for Structural Equation with Endogeneity

This paper studies variable selection problem in structural equation of a two-stage least squares (2SLS) model in presence of endogeneity which is commonly encountered in empirical economic studies. Model uncertainty and variable selection in the structural equation is an important issue as described in Andrews and Lu (2001) and Caner (2009). The authors propose an adaptive Lasso 2SLS estimator for linear structural equation with endogeneity and show that it enjoys the oracle properties, i.e., the consistency in both estimation and model selection. In Monte Carlo simulations, the authors demonstrate that the proposed estimator has smaller bias and MSE compared with the bridge-type GMM estimator (Caner, 2009). In a case study, the authors revisit the classic returns to education problem (Angrist and Krueger, 1991) using the China Population census data. The authors find that the education level not only has strong effects on income but also shows heterogeneity in different age cohorts.     

Smoothed estimator of quantile residual lifetime for right censored data

It is of great interest to estimate quantile residual lifetime in medical science and many other ?elds. In survival analysis, Kaplan-Meier(K-M) estimator has been widely used to estimate the survival distribution. However, it is well-known that the K-M estimator is not continuous, thus it can not always be used to calculate quantile residual lifetime. In this paper, the authors propose a kernel smoothing method to give an estimator of quantile residual lifetime. By using modern empirical process techniques, the consistency and the asymptotic normality of the proposed estimator are provided neatly.The authors also present the empirical small sample performances of the estimator. De?ciency is introduced to compare the performance of the proposed estimator with the naive unsmoothed estimator of the quantile residaul lifetime. Further simulation studies indicate that the proposed estimator performs very well.     

Nonparametric regression under double-sampling designs

This paper studies nonparametric estimation of the regression function with surrogate outcome data under double-sampling designs, where a proxy response is observed for the full sample and the true response is observed on a validation set. A new estimation approach is proposed for estimating the regression function. The authors first estimate the regression function with a kernel smoother based on the validation subsample, and then improve the estimation by utilizing the information on the incomplete observations from the non-validation subsample and the surrogate of response from the full sample. Asymptotic normality of the proposed estimator is derived. The effectiveness of the proposed method is demonstrated via simulations.     

Optimal allocation for estimating the correlation coefficient of Morgenstern type bivariate exponential distribution by ranked set sampling with concomitant variable

Ranked set sample is applicable whenever ranking of a set of sample units can be done easily by a judgement method of the study variable or of the auxiliary variable. This paper considers ranked set sample based on the auxiliary variable X which is correlated with the study variable Y, where (X, Y) follows Morgenstern type bivariate exponential distribution. The authors discuss the optional allocation for unbiased estimators of the correlation coefficient ρ of the random variables X and Y when the auxiliary variable X is used for ranking the sample units and the study variable Y is measured for estimating the correlation coefficient. This paper first gives a class of unbiased estimators of ρ when the mean θ of the study variable Y is known and obtains an essentially complete subclass of this class. Further, the optimal allocation of the unbiased estimators is found in this subclass and is proved to be Bayes, admissible, and minimax. Finally, the unbiased estimator of ρ under the optimal allocation in the case of known θ is reformed for estimating ρ in the case of unknown θ, and the reformed estimator is shown to be strongly consistent.     

Sensor selection for random field estimation in wireless sensor networks

This paper studies the sensor selection problem for random field estimation in wireless sensor networks.The authors first prove that selecting a set of l sensors that minimize the estimation error under the D-optimal criterion is NP-complete.The authors propose an iterative algorithm to pursue a suboptimal solution.Furthermore,in order to improve the bandwidth and energy efficiency of the wireless sensor networks,the authors propose a best linear unbiased estimator for a Gaussian random field with quantized measurements and study the corresponding sensor selection problem.In the case of unknown covariance matrix,the authors propose an estimator for the covariance matrix using measurements and also analyze the sensitivity of this estimator.Simulation results show the good performance of the proposed algorithms.     

Linear Bayes estimators applied to the inverse Gaussian lifetime model

For the two-parameter inverse Gaussian distribution denoted by I G(μ,λ),the authors employ a linear Bayes procedure to estimate the parameters μ and λ.The superiority of the proposed linear Bayes estimator(LBE) over both the classical UMVUE and the maximum likelihood estimator(MLE) is established in terms of the mean squared error matrix(MSEM) criterion.Compared with the usual Bayes estimator,which is obtained by an MCMC method,the proposed LBE is simple and easy to use.Some numerical results are presented to verify that the LBE performs well.     

Variable selection for recurrent event data with informative censoring

Recurrent events data with a terminal event(e.g.,death) often arise in clinical and observational studies.Variable selection is an important issue in all regression analysis.In this paper, the authors first propose the estimation methods to select the significant variables,and then prove the asymptotic behavior of the proposed estimator.Furthermore,the authors discuss the computing algorithm to assess the proposed estimator via the linear function approximation and generalized cross validation method for determination of the tuning parameters.Finally,the finite sample estimation for the asymptotical covariance matrix is also proposed.     

Semiparametric analysis of isotonic errors-in-variables regression models with randomly right censored response

This paper considers the estimation of a semiparametric isotonic regression model when the covariates are measured with additive errors and the response is randomly right censored by a censoring time. The authors show that the proposed estimator of the regression parameter is rootn consistent and asymptotically normal. The authors also show that the isotonic estimator of the functional component, at a fixed point, is cubic root-n consistent and converges in distribution to the slope at zero of the greatest convex minorant of the sum of a two-sided standard Brownian motion and the square of the time parameter. A simulation study is carried out to investigate the performance of the estimators proposed in this article.     

潜艇隐蔽攻击能力分析方法研究

纯方位定位系统是复杂的非线性系统,所有的估计算法都是次优的。理想估计器是工程估计器的性能上限,在理想估计器下的潜艇隐蔽攻击效率是最高的,针对其在工程上无法实现,讨论了这种最大值的数量特性。提出了“隐蔽攻击能力上限”的概念,分别给出了求解估计器精度和收敛时间意义下隐蔽攻击能力上限的仿真流程。最后,进行了计算机仿真试验和结果分析,得到了一些有价值的结论。     

Asymptotic Optimality of the Nonnegative Garrote Estimator Under Heteroscedastic Errors

This paper proposes the Nonnegative Garrote(NG) estimator for linear model with heteroscedastic errors. On the other hand, under some regularity conditions, the authors show the asymptotic optimality of the NG estimator by referring to the idea of the asymptotic optimality of the model average estimator. Simulation results and a real data analysis are reported for testing the results obtained previously. These results provide a stronger theoretical basis for the use of NG estimator by strengthening existing findings.     

基于HPA特性曲线拟合的自适应预失真技术

提出了一种用于正交频分复用(OFDM)系统中补偿由大功率放大器引起的非线性失真的基带自适应预失真方法。自适应预失真器是基于对大功率放大器的非线性特性进行分段估计,由曲线拟合逐段取逆变换来训练大功率放大器的预失真器的非线性参数,以达到预是真补偿的目的。计算机仿真结果表明,该方法具有收敛速度快、需要动态存储器(RAM)少、补偿效果好等特点。     

Improved anovae of the covariance matrix in general linear mixed models

In this paper, the problem of estimating the covariance matrix in general linear mixed models is considered. A new class of estimators is proposed. It is shown that this new estimator dominates the analysis of variance estimate under two squared loss functions. Finally, some simulation results to compare the performance of the proposed estimator with that of the analysis of variance estimate are reported. The simulation results indicate that this new estimator provides a substantial improvement in risk under most situations.     

Further result in the fast and accurate estimation of single frequency

A new fast and accurate method for estimating the frequency of a complex sinusoid in complex white Gaussian environments is proposed.The new estimator comprises of applications of low-pass filtering,decimation, and frequency estimation by linear prediction.It is computationally efficient yet obtains the Cramer-Rao bound at moderate signal-to-noise ratios.And it is well suited for real time applications requiring precise frequency estimation.Simulation results are included to demonstrate the performance of the proposed method.     

风险度量半参数变系数复合Expectile回归模型及应用

本文结合半参数变系数回归模型、期望分位数风险价值(EVaR)的思想以及充分利用多个Expectile信息能提高参数估计效率的假设,提出了一类半参数变系数复合Expectile回归模型,并对该模型进行了估计,建立了所提出复合Expectile回归(CER)估计的大样本性质.针对该模型既含有参数部分也含有非参数部分的特征,采用了方便计算的三步估计方法.通过数值模拟也发现,当误差为厚尾或非对称分布时,在均方根误差(RMSE)的标准下,所提出的CER估计大大优于最小二乘(LS)估计和简单的Expectile回归(ER)估计.另外,本文还应用所发展的理论分析了我国货币政策对上证综指的影响.     

现场可靠性数据的变点分析

针对产品寿命分布规律在使用过程中的变点问题,提出了产品寿命分布变点检验和估计的Bayes方法。根据产品现场使用先验信息的结构特点,提出了变点先验分布的确定方法,并给出了变点Bayes估计的计算方法。利用所得到的变点估计可制定产品的最佳维修策略以及产品报废期。数值模拟表明Bayes方法具有较好的稳健性,适用于现场可靠性数据的变点分析。     

Feature Screening for Nonparametric and Semiparametric Models with Ultrahigh-Dimensional Covariates

This paper considers the feature screening and variable selection for ultrahigh dimensional covariates. The new feature screening procedure base on conditional expectation which is used to differentiate whether an explanatory variable contributes to a response variable or not, without requiring a specific parametric form of the underlying data model. The authors estimate the marginal conditional expectation by kernel regression estimator. The proposed method is showed to have sure screen property. The authors propose an iterative kernel estimator algorithm to reduce the ultrahigh dimensionality to an appropriate scale. Simulation results and real data analysis demonstrate the proposed method works well and performs better than competing methods.     

声速不确定条件下的运动水下航行器自定位

传统利用水下声学定位系统的航行器自定位存在两方面问题, 一方面没有考虑测量周期内航行器的运动, 另一方面没有考虑水声声速的不确定。为解决上述问题, 构建了航行器运动状态下的时间测量定位模型, 并对声速不确定性进行建模。分别推导了基于加权最小二乘的运动航行器定位方法和基于最大似然估计的声速更新方法。利用所提模型和时间测量, 不仅可以估计航行器位置, 还可以更新声速。仿真结果验证了本文所提方法在各种参数设置下均优于现有方法, 并且在时间测量噪声不大时可以达到克拉美罗下界(Cramer-Rao lower bound, CRLB), 在时间误差不大时, 声速更新结果显著提高。     

基于Haar小波变换的信号到达时间估计

研究了在信号频率、初相、幅度未知条件下,矩形包络复正弦信号的到达时间估计算法。先利用相关检测算法,对信号的起止时间进行粗估计。在检测到信号的条件下,估计出信号频率并将其变换至基带,在一定尺度下对基带信号作Haar小波变换,检测出小波变换模值的峰点位置作为到达时间精估计。给出了小波尺度选取的原则,并推导了到达时间估计的克拉美罗限(CRLB)。计算机仿真表明,信噪比达6 dB时,到达时间估计的均方根误差小于0.8倍的采样间隔,实现了对信号到达时间的精估计。     

New Moment Estimators of the Effective Spread Based on Daily High and Low Prices

This paper proposes five new simple moment estimators of the effective spread based on the covariance estimator of Roll(1984) and the High-Low estimator recently developed by Corwin and Schultz(2012). And then the authors theoretically investigate the statistical properties of six simple High-Low spread estimators including Corwin and Schultz's estimator. The biases and mean squared errors(MSE) of these six estimators have been derived and compared with each other asymptotically,which, together with the subsequent simulation study, reveal explicitly the superior performance of newly developed High-Low estimators over Corwin and Schultz's estimator in both ideal and nonideal conditions. Moreover, this paper also develops GMM estimators constructed by three or more moment conditions and compares with the six simple High-Low estimators. Finally, several example applications on the U.S. and Chinese financial markets are conducted to demonstrate the superior performance of the new High-Low estimators. The results provide alternative choices for identifying the liquidity proxies that well capture different structure of markets.