Should structure functions be used to estimate power laws in turbulence? A comparative study

Second-order structure functions are widely used to characterize turbulence in the inertial range because they are simple to estimate, particularly in comparison to spectral density functions and wavelet variances. Structure function estimators, however, are highly autocorrelated and, as a result, no suitable theory has been established to provide confidence intervals for turbulence parameters when determined via regression fits in log/log space. Monte Carlo simulations were performed to compare the performance of structure function estimators of turbulence parameters with corresponding multitaper spectral and wavelet variance estimators. The simulations indicate that these latter estimators have smaller variances than estimators based upon the structure function. In contrast to structure function estimators, the statistical properties of the multitaper spectral and wavelet variance estimators allow for the construction of confidence intervals for turbulence parameters. The Monte Carlo simulations also confirm the validity of the statistical theory behind the multitaper spectral and wavelet variance estimators. The strengths and weaknesses of the various estimators are further illustrated by analyzing an atmospheric temperature time series.     

改进的CS-UKF加速度方差自适应跟踪算法

针对基于当前统计模型的状态噪声协方差阵中的加速度方差调整方法对一般机动目标、非机动目标跟踪精度差的问题,研究其改进方法;在建立机动目标当前统计模型离散状态方程和雷达导引头离散观测方程的基础上;利用雷达导引头测量信息和位置预测值之间的扰动对加速度方差进行调整,提出了改进的加速度方差自适应调整无迹卡尔曼滤波跟踪算法;数字仿真验证了该算法对非机动目标、一般机动目标以及高机动目标均具有良好的跟踪效果。     

基于统计特征的轮胎纹理缺陷在线检测

根据轮胎X光纹理缺陷区域灰度及灰度分布异常的特点,研究了一种通过分析统计特征进行在线缺陷检测的方法。在轮胎X光纹理灰度分布模型基础上,采用正则化预处理去除背景噪声,然后进行图像分块,分别计算每块的灰度均值和方差,并采用双线性插值运算形成均值图像和方差图像,再通过二值化实现缺陷检测。实验表明,与人工检测方法进行对比,该方法误判率低,检测精度高,并且运算速度快,能满足在线检测要求。     

Quantum detection and estimation theory

A review. Quantum detection theory is a reformulation, in quantum-mechanical terms, of statistical decision theory as applied to the detection of signals in random noise. Density operators take the place of the probability density functions of conventional statistics. The optimum procedure for choosing between two hypotheses, and an approximate procedure valid at small signal-to-noise ratios and called threshold detection, are presented. Quantum estimation theory seeks best estimators of parameters of a density operator. A quantum counterpart of the Cramér-Rao inequality of conventional statistics sets a lower bound to the mean-square errors of such estimates. Applications at present are primarily to the detection and estimation of signals of optical frequencies in the presence of thermal radiation.This paper was prepared under grant NGR-05-009-079 from the National Aeronautics and Space Administration.     

On Generalized Schürmann Entropy Estimators

We present a new class of estimators of Shannon entropy for severely undersampled discrete distributions. It is based on a generalization of an estimator proposed by T. Schürmann, which itself is a generalization of an estimator proposed by myself.For a special set of parameters, they are completely free of bias and have a finite variance, something which is widely believed to be impossible. We present also detailed numerical tests, where we compare them with other recent estimators and with exact results, and point out a clash with Bayesian estimators for mutual information.     

Spatial Warped Gaussian Processes: Estimation and Efficient Field Reconstruction

A class of models for non-Gaussian spatial random fields is explored for spatial field reconstruction in environmental and sensor network monitoring. The family of models explored utilises a class of transformation functions known as Tukey g-and-h transformations to create a family of warped spatial Gaussian process models which can support various desirable features such as flexible marginal distributions, which can be skewed, leptokurtic and/or heavy-tailed. The resulting model is widely applicable in a range of spatial field reconstruction applications. To utilise the model in applications in practice, it is important to carefully characterise the statistical properties of the Tukey g-and-h random fields. In this work, we study both the properties of the resulting warped Gaussian processes as well as using the characterising statistical properties of the warped processes to obtain flexible spatial field reconstructions. In this regard we derive five different estimators for various important quantities often considered in spatial field reconstruction problems. These include the multi-point Minimum Mean Squared Error (MMSE) estimators, the multi-point Maximum A-Posteriori (MAP) estimators, an efficient class of multi-point linear estimators based on the Spatial-Best Linear Unbiased (S-BLUE) estimators, and two multi-point threshold exceedance based estimators, namely the Spatial Regional and Level Exceedance estimators. Simulation results and real data examples show the benefits of using the Tukey g-and-h transformation as opposed to standard Gaussian spatial random fields in a real data application for environmental monitoring.     

Long memory in patterns of mobile phone usage

In this article we show that usage of a mobile phone, i.e. daily series of number of calls made by a customer, exhibits long memory. We use a sample of 4502 postpaid users from a Polish mobile operator and study their two-year billing history. We estimate Hurst exponent by nine estimators: aggregated variance method, differencing the variance, absolute values of the aggregated series, Higuchi’s method, residuals of regression, the R/S method, periodogram method, modified periodogram method and Whittle estimator. We also analyze empirically relations between estimators. Long memory implies an inertial effect in clients’ behavior which may be used by mobile operators to accelerate usage and gain additional profit.     

Non-analog Monte Carlo estimators for radiation momentum deposition

The standard method for calculating radiation momentum deposition in Monte Carlo simulations is the analog estimator, which tallies the change in a particle's momentum at each interaction with the matter. Unfortunately, the analog estimator can suffer from large amounts of statistical error. In this paper, we present three new non-analog techniques for estimating momentum deposition. Specifically, we use absorption, collision, and track-length estimators to evaluate a simple integral expression for momentum deposition that does not contain terms that can cause large amounts of statistical error in the analog scheme. We compare our new non-analog estimators to the analog estimator with a set of test problems that encompass a wide range of material properties and both isotropic and anisotropic scattering. In nearly all cases, the new non-analog estimators outperform the analog estimator. The track-length estimator consistently yields the highest performance gains, improving upon the analog-estimator figure of merit by factors of up to two orders of magnitude.     

Portfolio Efficiency Tests with Conditioning Information—Comparing GMM and GEL Estimators

We evaluate the use of generalized empirical likelihood (GEL) estimators in portfolio efficiency tests for asset pricing models in the presence of conditional information. The use of conditional information is relevant to portfolio management as it allows for checking whether asset allocations are efficiently exploiting all the information available in the market. Estimators from the GEL family present some optimal statistical properties, such as robustness to misspecifications and better properties in finite samples. Unlike generalized method of moments (GMM) estimators, the bias for GEL estimators does not increase with the number of moment conditions included, which is expected in conditional efficiency analysis. Due to these better properties in finite samples, our main hypothesis is that portfolio efficiency tests using GEL estimators may have better properties in terms of size, power, and robustness. Using Monte Carlo experiments, we show that GEL estimators have better performance in the presence of data contaminations, especially under heavy tails and outliers. Extensive empirical analyses show the properties of the estimators for different sample sizes and portfolio types for two asset pricing models.     

Studies in nonlinear stochastic processes. II. The duffing oscillator revisited

We have applied the approximation method of statistical linearization and various higher order corrections thereto to the study of a nonlinear oscillator perturbed by Gaussian, delta-correlated noise. We compute the second-order statistics of the response, i.e., the variances, autocorrelation functions, and spectral densities for various forms of the nonlinearity and compare our results with the few more exact calculations which are available in the literature. We show that a very simple modification of statistical linearization, based upon the use of the variance as obtained from the appropriate Fokker-Planck equation, yields results which are in better agreement with the exact literature results than either statistical linearization or first-order corrections thereto. This modified method of statistical linearization has the significant advantage of great computational simplicity as compared to other attempts of accurate calculations of second-order statistics of nonlinear stochastic equations now in the literature.This work was supported in part by the National Science Foundation under Grants MPS 72-04363 and CHE 75-20624.     

Noise estimation in single- and multiple-coil magnetic resonance data based on statistical models

Noise estimation is a challenging task in magnetic resonance imaging (MRI), with applications in quality assessment, filtering or diffusion tensor estimation. Main noise estimators based on the Rician model are revisited and classified in this article, and new useful methods are proposed. Additionally, all the surveyed estimators are extended to the noncentral chi model, which applies to multiple-coil MRI and some important parallel imaging algorithms for accelerated acquisitions. The proposed new noise estimation procedures, based on the distribution of local moments, show better performance in terms of smaller variance and unbiased estimation over a wide range of experiments, with the additional advantage of not needing to explicitly segment the background of the image.     

非广延统计力学与完全开放系统的统计分布

简介了非广延统计力学的Tsallis统计,用其计算了理想气体;推导出了以含有非广延熵常数的Shannon熵为基础和以Tsallis熵为基础的非广延统计力学的完全开放系统的统计分布及计算热力学量的公式;讨论表明:Tsallis熵对应的统计分布及计算热力学量的公式在非广延参量q→1时,完全过渡到了Shannon熵对应的形式.     

Optimal least-squares estimators of the diffusion constant from a single Brownian trajectory

Modern developments in microscopy and image processing are revolutionising areas of physics, chemistry, and biology as nanoscale objects can be tracked with unprecedented accuracy. However, the price paid for having a direct visualisation of a single particle trajectory with high temporal and spatial resolution is a consequent lack of statistics. This naturally calls for reliable analytical tools which will allow one to extract the properties specific to a statistical ensemble from just a single trajectory. In this article we briefly survey different analytical methods currently used to determine the ensemble average diffusion coefficient from single particle data and then focus specifically on weighted least-squares estimators, seeking the weight functions for which such estimators are ergodic. Finally, we address the question of the effects of disorder on such estimators.     

前后向子分段相位差频率估计法

为提高直接频率估计法的精度并推导出估计误差方差的闭合表达式,本文提出基于前后向子分段相位差的直接频率估计法. 该方法对样本的前向和后向两个子段做快速傅里叶变换,再提取这两个子段变换后的峰值谱相位差而获得频率估计. 本文证明了该估计器具有无偏性,并推导出其频率估计方差的闭合理论表达式. 仿真实验验证了该闭合表达式的正确性,故本文方法具有更高的测频精度和广泛的应用前景. 关键词： 频率估计 前后向子段 相位差 克拉美罗限     

Minimum Divergence Estimators,Maximum Likelihood and the Generalized Bootstrap

This paper states that most commonly used minimum divergence estimators are MLEs for suited generalized bootstrapped sampling schemes. Optimality in the sense of Bahadur for associated tests of fit under such sampling is considered.     

On the systematic bias in the estimation of black hole masses in active galactic nuclei

In this report,we find the MBH estimated from the formalism of Wang et al.are more consistent with those from the MBH-σ relation than those from previous single-epoch mass estimators,using a large sample of AGNs.Furthermore,we examine the diferences between the line widths of Hβ and Mg Ⅱ in detail by comparing their line profiles.The flux around the line core and that in the wing of both Hβ and Mg Ⅱ show an opposite variation tendency,which indicates the BLR is multi-componential.The contribution of the wing makes the FWHM deviate fromσline,and thus bias the MBH estimated from previous single-epoch mass estimators.Thus the correction on the formalism suggested by Wang et al.is crucial to MBH estimation.     

A Framework for Non-Equilibrium Statistical Ensemble Theory

Since Gibbs synthesized a general equilibrium statistical ensemble theory, many theorists have attempted to generalized the Gibbsian theory to
non-equilibrium phenomena domain, however the status of the theory of non-equilibrium phenomena can not be said as firm as well established as the
Gibbsian ensemble theory. In this work, we present a framework for the non-equilibrium statistical ensemble formalism based on a subdynamic kinetic
equation (SKE) rooted from the Brussels-Austin school and followed by some up-to-date works. The constructed key is to use a similarity transformation between Gibbsian ensembles formalism based on Liouville equation and the subdynamic ensemble formalism based on the SKE. Using this formalism, we study the spin-Boson system, as cases of weak coupling or strongly coupling, and obtain the reduced density operators for the Canonical ensembles easily.     

Statistical mechanics of learning: a variational approach for real data

Using a variational technique, we generalize the statistical physics approach of learning from random examples to make it applicable to real data. We demonstrate the validity and relevance of our method by computing approximate estimators for generalization errors that are based on training data alone.     

Can statistical average be affected by its observer?

In classical statistical mechanics, it is tacitly assumed that the only side effect of the measuring process is to produce fluctuations in the measured system. In other words, the system's average property is not affected by the observer. The present letter discusses this assumption in terms of aGedankenexperiment.     

Statistical variance reduction analysis in counting the highly overlapping nuclear tracks

In this paper, a statistical analysis is performed on counting the highly overlapping tracks. As the track density and tracks overlapping increase, the statistical error of track counting raises. An upper limit for the uncertainty of the track counting is found. The approximated number of tracks is computed based on the measured number of objects, approximated ratio of overlapping cases, measured number of tracks in the overlapping cases and calculated uncertainty. The approximation of the number of tracks obtained through both calculation and measurement processes is validated comparing with the real number of manually counted tracks and the uncertainty is calculated for each comparison.