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Three questions are discussed in this paper, 1. The application of maximum likelihood estimation to the Weibull distribution and its flow charts are discussed, 2. By means of Monte-Carlo method, it is pointed out that the maximum likelihood estimator of the shape parameter is biased in the cases of both complete and censored samples if the sample size is not big enough. Unbiasing factors are presented here. 3. Some valuable conclusions are obtained for sejecting the most most effective censoring...

Three questions are discussed in this paper, 1. The application of maximum likelihood estimation to the Weibull distribution and its flow charts are discussed, 2. By means of Monte-Carlo method, it is pointed out that the maximum likelihood estimator of the shape parameter is biased in the cases of both complete and censored samples if the sample size is not big enough. Unbiasing factors are presented here. 3. Some valuable conclusions are obtained for sejecting the most most effective censoring and sample size.

1.最大似然估计在Weibull分布上的实际运用和计算框图。 2.用Monte-carlo法研究后指出,在子样容量不大时,不论是完全试验还是截尾试验,形状参数b的最大似然估计是有偏的,并给出了用Monte-Carlo法得到的修偏系数表,供读者参考使用。 3.如何确定最经济的试验子样容量n和截尾数r,本文也给出了一些有益的结论。

On the basis of available theoretical results of maximum entropy spectrumestimation (MESE), a study has been made of segmented maximum entropyspectrum estimation (SMESE). Using the method of Monte-Carlo simulation, weproved that under certain conditions SMESE can improve the estimation qualityof spectrum estimation. For the spectrum estimation of Gauss autoregressivestationary process several different methods of SMEM were investigated and itwas proved that "the maximum likelihood estimation of the order",...

On the basis of available theoretical results of maximum entropy spectrumestimation (MESE), a study has been made of segmented maximum entropyspectrum estimation (SMESE). Using the method of Monte-Carlo simulation, weproved that under certain conditions SMESE can improve the estimation qualityof spectrum estimation. For the spectrum estimation of Gauss autoregressivestationary process several different methods of SMEM were investigated and itwas proved that "the maximum likelihood estimation of the order", and "the sa-mple average of the autoregressive coefficients" gave better results.

简要地回顾了MEM频谱估计的若干理论成果,用monte-Carlo方法证实“分段MEM谱频估计在一定条件下能够改善频谱估计性能,对高斯自回归平稳过程采用了几种不同的分段MEM频谱估计方法进行频谱估计,证实若采用“最大似然阶数估计”及“集平均自回归系数”可以得到较好的效果。

The paper investigated the relation between the sample number and the performances of signal detection and paramater estimation in correlative Gaussian noise within a fixed time T.It pointed out that when the autocorrelation coefficient between the neighbour samples is in the range of.0.1-0.2,the general SNR S2[T(XL)] will approach to the SNR limit S2(T).The paper also pointed out that the sample sequences of the solution of a second-order differential equation isn't generally an AR(2) model,but when the sample...

The paper investigated the relation between the sample number and the performances of signal detection and paramater estimation in correlative Gaussian noise within a fixed time T.It pointed out that when the autocorrelation coefficient between the neighbour samples is in the range of.0.1-0.2,the general SNR S2[T(XL)] will approach to the SNR limit S2(T).The paper also pointed out that the sample sequences of the solution of a second-order differential equation isn't generally an AR(2) model,but when the sample interval △→0,the sample sequences can be described by AR(2).Therefore we can calculate S2(T) easily.At the end of the paper,the relation between the likelihood ratio detection and the maximum likelihood estimation and that between their performance measurements were discussed.

本文研究了在确定的观察时间内,在有色高斯噪声中检测与估计的性能与样本数之间的关系。指出相邻样本之间相关系数在0.1—0.2范围内,广义信噪比就能相当接近极限值S~2(T)。在讨论二阶相关噪声时指出,由二阶微分方程描写的高斯过程的样本序列一般不是AR(2)模型,但是当样本间隔Δ→0时,却可用AR(2)模型近似描写序列,因此求极限信噪比时可以较简便地采用AR(2)模型。最后指出最大似然估计与似然比检验之间及两者的性能测度之间的联系。

 
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