An efficient and fast algorithm for estimating the frequencies of superimposed exponential signals in zero-mean multiplicative and additive noise

In this paper, we consider the superimposed exponential signals in zero-mean multiplicative and additive noise when all the noise are independently and identically distributed. We use a three-step iterative procedure to estimate the frequencies of the considered model. It is observed that the estimators are consistent and work quite well in terms of biases and mean square errors. Moreover, the convergence rate of the estimators attains O _p (N ^?3/2), which is the best convergence rate in the case of only additive noise and constant amplitude.     

Assessing direct and indirect seasonal decomposition in state space

The problem of whether seasonal decomposition should be used prior to or after aggregation of time series is quite old. We tackle the problem by using a state-space representation and the variance/covariance structure of a simplified one-component model. The variances of the estimated components in a two-series system are compared for direct and indirect approaches and also to a multivariate method. The covariance structure between the two time series is important for the relative efficiency. Indirect estimation is always best when the series are independent, but when the series or the measurement errors are negatively correlated, direct estimation may be much better in the above sense. Some covariance structures indicate that direct estimation should be used while others indicate that an indirect approach is more efficient. Signal-to-noise ratios and relative variances are used for inference.     

Spectral Estimation of Covolatility from Noisy Observations Using Local Weights

We propose localized spectral estimators for the quadratic covariation and the spot covolatility of diffusion processes, which are observed discretely with additive observation noise. The appropriate estimation for time‐varying volatilities is based on an asymptotic equivalence of the underlying statistical model to a white‐noise model with correlation and volatility processes being constant over small time intervals. The asymptotic equivalence of the continuous‐time and discrete‐time experiments is proved by a construction with linear interpolation in one direction and local means for the other. The new estimator outperforms earlier non‐parametric methods in the literature for the considered model. We investigate its finite sample size characteristics in simulations and draw a comparison between various proposed methods.     

Geometric Anisotropic Spatial Point Pattern Analysis and Cox Processes

We consider spatial point processes with a pair correlation function, which depends only on the lag vector between a pair of points. Our interest is in statistical models with a special kind of ‘structured’ anisotropy: the pair correlation function is geometric anisotropic if it is elliptical but not spherical. In particular, we study Cox process models with an elliptical pair correlation function, including shot noise Cox processes and log Gaussian Cox processes, and we develop estimation procedures using summary statistics and Bayesian methods. Our methodology is illustrated on real and synthetic datasets of spatial point patterns.     

Explicit analytical solutions for ARL of CUSUM chart for a long-memory SARFIMA model

This paper aims to derive explicit analytical solutions for Average Run Length (ARL) of CUSUM chart for the SARFIMA(P,D,Q)_S process with exponential white noise. Measurement of performance was done with the ARL in terms of percentage error and CPU time. The results obtained from the explicit formulas were compared focusing on the performance using the numerical integral equation (NIE) method. Both methods had similarly excellent agreement with the percentage error at less than 0.25%. Meanwhile, the explicit formulas consumed less CPU time than the NIE method. It is clear that the explicit formulas are a good alternative in real applications.     

时变三因子模型风险系数的动态研究

由Fama和French提出的三因子模型能够较好地解释股票的收益率风险溢价。文章以状态空间模型为框架,将风险因子系数作为状态变量,市场风险溢价作为观测变量,构建时变三因子模型来应对股票市场价格的时变特征。研究结果显示,利用卡尔曼滤波来估计时变风险因子系数,增强了估计结果的准确性与连贯性;风险因子系数变化规律与中国A股市场政策和环境影响相吻合,消除非理性噪声后的时变三因子模型更具有解释力度。     

微弱荧光信号检测放大器的低噪声设计

本文对采用光电二极管的荧光信号接收电路的工作模式和噪声进行了分析。并论述了输入失调和暗电流的补偿方法,深入讨论了反馈电阻RF及补偿电容CF的防泄漏措施、输入管脚的“护卫(guarding)”与隔离技术、输入管脚的悬置引出技术、PCB板的清洁和隔离涂敷、屏蔽和滤波等技术。使所设计的接收电路获得较高的信噪比(SNR)。     

光热反射实验中微弱信号的测量

介绍了用激光光热反射方法测量表面下物体热物性的实验原理.分析了实验中噪声的主要来源,运用带通滤光片结合差分探测的方式降低激光器产生的噪声.锁相放大技术对被测信号进行窄带放大,锁相放大器结合示波器输出了被测信号的周期变化部分,实现了光热法测量中微弱信号的测量.     

W波段小型化锁相源

根据基波锁相、谐波提取的思想,采用双环数字锁相方式,实现了Ｗ 波段二次谐波耿氏锁相振荡器;分析了混频锁相环中输出端口与中频端口相位抖动的关系,指出了从自身中频端口测试相噪的局限性。该锁相源频率为９３ ＧＨｚ,射频偏离载波５０ ＫＨｚ 时,相噪为－ ７０ ｄＢｃ／Ｈｚ ,输出功率大于１０ ｍ Ｗ,可用于本振等小功率场合