基于Tabu搜索算法的似然性估计在边界检测中的运用

边界检测一直是图象分析处理的重点和难点。现在的边界检测算法(如K-L变换、Sobel算子的梯度检测、模板检测等)在不同程度上存在着抗噪声能力不强、算法计算量过大的缺点,不适于实时应用。新出现的一批智能化自适应算法(如Tabu搜索算法、遗传算法等)在此方面有很大改进。本文主要介绍Tabu算法在边界检测中的运用。

一类分类数据列联表中基于检验功效的样本量研究

对不完全2×2列联表中关于风险比(RR)的假设检验问题,使用基于约束性极大似然估计下的Wald检验统计量和对数变换检验统计量,导出了满足预先给定功效的样本量公式.模拟结果验证了所给检验和样本量公式的合理性,实例分析解释了上述方法的应用.

Diagnostics in generalized nonlinear models based on maximum L_q-likelihood estimation

In order to detect whether the data conforms to the given model, it is necessary to diagnose the data in the statistical way. The diagnostic problem in generalized nonlinear models based on the maximum Lq-likelihood estimation is considered. Three diagnostic statistics are used to detect whether the outliers exist in the data set. Simulation results show that when the sample size is small, the values of diagnostic statistics based on the maximum Lq-likelihood estimation are greater than the values based on the maximum likelihood estimation. As the sample size increases, the difference between the values of the diagnostic statistics based on two estimation methods diminishes gradually. It means that the outliers can be distinguished easier through the maximum Lq-likelihood method than those through the maximum likelihood estimation method.

Retrieval algorithm of sea surface wind vectors for WindSat based on a simple forward model

WindSat/Coriolis is the first satellite-borne polarimetric microwave radiometer, which aims to improve the potential of polarimetric microwave radiometry for measuring sea surface wind vectors from space. In this paper, a wind vector retrieval algorithm based on a novel and simple forward model was developed for WindSat. The retrieval algorithm of sea surface wind speed was developed using multiple linear regression based on the simulation dataset of the novel forward model. Sea surface wind directions that minimize the difference between simulated and measured values of the third and fourth Stokes parameters were found using maximum likelihood estimation, by which a group of ambiguous wind directions was obtained. A median filter was then used to remove ambiguity of wind direction. Evaluated with sea surface wind speed and direction data from the U.S. National Data Buoy Center （NDBC）, root mean square errors are 1.2 rn/s and 30~ for retrieved wind speed and wind direction, respectively. The evaluation results suggest that the simple forward model and the retrieval algorithm are practicable for near-real time applications, without reducing accuracy.

Characteristics for wind energy and wind turbines by considering vertical wind shear

The probability distributions of wind speeds and the availability of wind turbines were investigated by considering the vertical wind shear. Based on the wind speed data at the standard height observed at a wind farm, the power-law process was used to simulate the wind speeds at a hub height of 60 m. The Weibull and Rayleigh distributions were chosen to express the wind speeds at two different heights. The parameters in the model were estimated via the least square(LS) method and the maximum likelihood estimation(MLE) method, respectively. An adjusted MLE approach was also presented for parameter estimation. The main indices of wind energy characteristics were calculated based on observational wind speed data. A case study based on the data of Hexi area, Gansu Province of China was given. The results show that MLE method generally outperforms LS method for parameter estimation, and Weibull distribution is more appropriate to describe the wind speed at the hub height.

不完全2×2列联表中基于置信区间的样本量研究

使用基于约束性极大似然估计的统计量,对不完全2×2列联表中的风险比进行置信区间构造.导出在一定置信水平下控制置信区间宽度的样本量公式.模拟研究和实例分析验证了所给方法的合理性.

Passive source localization using importance sampling based on TOA and FOA measurements

Passive source localization via a maximum likelihood （ML） estimator can achieve a high accuracy but involves high calculation burdens, especially when based on time-of-arrival and frequency-of-arrival measurements for its internal nonlinearity and nonconvex nature. In this paper, we use the Pincus theorem and Monte Carlo importance sampling （MCIS） to achieve an approximate global solution to the ML problem in a computationally efficient manner. The main contribution is that we construct a probability density function （PDF） of Gaussian distribution, which is called an important function for efficient sampling, to approximate the ML estimation related to complicated distributions. The improved performance of the proposed method is at- tributed to the optimal selection of the important function and also the guaranteed convergence to a global maximum. This process greatly reduces the amount of calculation, but an initial solution estimation is required resulting from Taylor series expansion. However, the MCIS method is robust to this prior knowledge for point sampling and correction of importance weights. Simulation results show that the proposed method can achieve the Cram6r-Rao lower bound at a moderate Gaussian noise level and outper- forms the existing methods.

Corrected empirical likelihood for a class of generalized linear measurement error models

Generalized linear measurement error models, such as Gaussian regression, Poisson regression and logistic regression, are considered. To eliminate the effects of measurement error on parameter estimation, a corrected empirical likelihood method is proposed to make statistical inference for a class of generalized linear measurement error models based on the moment identities of the corrected score function. The asymptotic distribution of the empirical log-likelihood ratio for the regression parameter is proved to be a Chi-squared distribution under some regularity conditions. The corresponding maximum empirical likelihood estimator of the regression parameter π is derived, and the asymptotic normality is shown. Furthermore, we consider the construction of the confidence intervals for one component of the regression parameter by using the partial profile empirical likelihood. Simulation studies are conducted to assess the finite sample performance. A real data set from the ACTG 175 study is used for illustrating the proposed method.

AN ALGORITHM FOR MAXIMUM LIKELIHOOD ESTIMATES TO SEMI-PARAMETRIC REGRESSION MODEL WITH INTERVAL CENSORED DATA

In this paper we study the practical procedure for getting the maximumlikelihood estimates in a semi-parametric regression model with interval censored data.On the basis of the on previous theoretical results, we give the detailed algorithms when there are one or two covariates in the model.

EMPIRICAL LIKELIHOOD FOR LONGITUDINAL PARTIALLY LINEAR MODEL WITH α-MIXING ERRORS

This paper considers large sample inference for the regression parameter in a partially linear regression model with longitudinal data and a-mixing errors. The authors introduce an estimated empirical likelihood for the regression parameter and show that its limiting distribution is a mixture of central chi-squared distributions. Also, the authors derive an adjusted empirical likelihood method which is shown to have a central chi-square limiting distribution. A simulation study is carried out to assess the performance of the empirical likelihood method.

Analysis of seasonal signals and long-term trends in the height time series of IGS sites in China

The seasonal signal and long-term trend in the height time series of 10 IGS sites in China are investigated in this paper. The offset detection and outlier removal as well as the removal of common mode error are performed on the raw GPS time-series data developed by the Scripps Orbit and Permanent Array Center(SOPAC). The seasonal-trend decomposition procedure based on LOESS(STL) is utilized to extract precise seasonal signals, followed by an estimation of the long-term trend with the application of maximum likelihood estimation(MLE) to the seasonally adjusted time series. The Up-compo- nents of all sites are featured by obvious seasonal variations, with significant phase and amplitude modulation on some sites. After Kendall's tau test, a significant trend(99% confidence interval) for all sites is achieved. Furthermore, the trends at sites TCMS and TNML have significant changes at epochs 2009.5384 and 2009.1493(95% confidence interval), respectively, using the Breaks For Additive Seasonal and Trend test. Finally, the velocities and their uncertainties for all sites are estimated using MLE with the white noise plus flicker noise model. And the results are analyzed and compared with those announced by SOPAC. The results obtained in this paper have a higher precision than the SOPAC results.

PARAMETER ESTIMATION OF EXPONENTIAL DISTRIBUTION

Because of the importance of grouped data, many scholars have been devoted to the study of this kind of data. But, few documents have been concerned with the thresh-old parameter. In this paper, we assume that the threshold parameter is smaller than the first observing point. Then, on the basis of the two-parameter exponential distribution, the maximum likelihood estimations of both parameters are given, the sufficient and necessary conditions for their existence and uniqueness are argued, and the asymptotic properties of the estimations are also presented, according to which approximate confidence intervals of the parameters are derived. At the same time, the estimation of the parameters is generalized, and some methods are introduced to get explicit expressions of these generalized estimations. Also, a special case where the first failure time of the units is observed is considered.