捕获-再捕获模型的统计学原理

捕获-再捕获模型由国外学者首创,最初用于野生动物总体规模估计,后来经过改进逐步应用于人口普查质量评估和其他统计领域。为了正确使用该模型,采取独一无二的方法,从试验背景、组格概率和边缘概率之间的关系、组格条件概率、条件多项分布和条件似然函数等方面对其进行全面解读和研究。研究表明:使用捕获-再捕获模型必须遵守三项理论原则:即总体封闭原则、个体同质原则、独立性原则;对实际问题与理论原则之间存在的差距必须做三件事情:即发现实际问题与理论原则之间的所有分歧点、评估各个分歧点问题的严重程度、寻找解决问题的办法。

Reliability evaluation for Weibull distribution under multiply type-? censoring

The multiply type-I censoring represented that all units in life test were terminated at different times. For estimations of Weibull parameters, it was easy to compute the maximum likelihood estimation （MLE） and least-squares estimation （LSE） while it was hard to build confidence intervals （CI）. The concept of generalized confidence interval （GCI） was introduced to build CIs of parameters under multiply type-I censoring. Further, GCI based on LSE and GCI based on MLE were proposed. It is mathematically proved that the former is exact and the latter is approximate. Besides, a Monte Carlo simulation study and an illustrative example also Ran out that the GCI method based on LSE yields rather satisfactory results by comparison with the ones based on MLE. It should be clear that the GCI method is a sensible choice to evaluate reliability under multiply type-I censoring.

Maximum likelihood channel estimation algorithm combines logarithm likelihood ratio decoding in the coal mine application

The environment of the wireless communication system in the coal mine has unique characteristics： great noise, strong multiple path interference, and the wireless communication of orthogonal frequency division multiplexing （OFDM） in underground coal mine is sensitive to the frequency selection of multiple path fading channel, whose decoding is separated from the traditional channel estimation algorithm. In order to increase its accuracy and reliability, a new iterating channel estimation algorithm combining the logarithm likelihood ratio （LLR） decode iterate based on the maximum likelihood estimation （ML） is proposed in this paper, which estimates iteration channel in combination with LLR decode. Without estimating the channel noise power, it exchanges the information between the ML channel estimation and the LLR decode using the feedback information of LLR decode. The decoding speed is very quick, and the satisfied result will be obtained by iterating in some time. The simulation results of the shortwave broadband channel in the coal mine show that the error rate of the system is basically convergent after the iteration in two times.

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.

Application of profile likelihood function to the uncertainty analysis of hydrometeorological extreme inference

Profile likelihood function is introduced to analyze the uncertainty of hydrometeorological extreme inference and the theory of estimating confidence intervals of the key parameters and quantiles of extreme value distribution by profile likelihood function is described.GEV(generalized extreme value)distribution and GP(generalized Pareto)distribution are used respectively to fit the annual maximum daily flood discharge sample of the Yichang station in the Yangtze River and the daily rainfall sample in10 big cities including Guangzhou.The parameters of the models are estimated by maximum likelihood method and the fitting results are tested by probability plot,quantile plot,return level plot and density plot.The return levels and confidence intervals of flood and rainstorm in different return periods are calculated by profile likelihood function.The results show that the asymmetry of the profile likelihood function curve increases with the return period,which can reflect the effect of the length of sample series and return periods on confidence interval.As an effective tool for estimating confidence interval of the key parameters and quantiles of extreme value distribution,profile likelihood function can lead to a more accurate result and help to analyze the uncertainty of extreme values of hydrometeorology.

EMPIRICAL LIKELIHOOD INFERENCE FOR LOGISTIC EQUATION WITH RANDOM PERTURBATION

Empirical likelihood(EL) combined with estimating equations(EE) provides a modern semi-parametric alternative to classical estimation techniques such as maximum likelihood estimation(MLE). This paper not only uses closed form of conditional expectation and conditional variance of Logistic equation with random perturbation to perform maximum empirical likelihood estimation(MELE) for the model parameters, but also proposes an empirical likelihood ratio statistic(ELRS) for hypotheses concerning the interesting parameter. Monte Carlo simulation results show that MELE and ELRS provide competitive performance to parametric alternatives.