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gamma分布
相关语句
  gamma distribution
    Factorial Analysis of Dependent Variable with Interval Data Fitted to Gamma Distribution
    含区间数据Gamma分布变量的因子分析
短句来源
    Parameter Estimation in Gamma Distribution with Interval Data
    含区间数据Gamma分布的参数估计
短句来源
    Conclusions MLE based on EM algorithm is robust for par ameter estimation of GLM based on Gamma distribution with interval data. GLM meth od can be used to explore factors that influencing SARS incubation period with i nterval data.
    结论 基于EM算法的MLE方法对于含区间数据Gamma分布GLM参数的估计是强健的,GLM方法可以用于含区间数据SARS潜伏期的影响因素分析。
短句来源
    Methods EM algorithm was employed to solve the maximum likelihood estimation (MLE) of parameters of general linear model (GLM) based on Gamma distribution with interval data,then univariate and multivariate anal yses were applied to explore factors that influen-cing SARS incubation period.
    方法 采用EM算法求解基于含区间数据Gamma分布的广义线性模型(GLM)参数的极大似然估计(MLE)。 采用所建立的GLM方法对含区间数据SARS潜伏期进行单因子、多因子分析。
短句来源
    Objective To develop a method to estimate the two parameters of Gamma distribution with interval data and conduct that to estimate the length of incubation period of severe acute respiratory syndrome (SARS).
    目的建立含区间数据Gamma分布的参数估计方法,并用于SARS潜伏期的推算。
短句来源
更多       
  gam ma distribution
    Factorial Analysis of Dependent Variable with Interval Data Fitted to Gamma Distribution
    含区间数据Gamma分布变量的因子分析
短句来源
    Parameter Estimation in Gamma Distribution with Interval Data
    含区间数据Gamma分布的参数估计
短句来源
    Conclusions MLE based on EM algorithm is robust for par ameter estimation of GLM based on Gamma distribution with interval data. GLM meth od can be used to explore factors that influencing SARS incubation period with i nterval data.
    结论 基于EM算法的MLE方法对于含区间数据Gamma分布GLM参数的估计是强健的,GLM方法可以用于含区间数据SARS潜伏期的影响因素分析。
短句来源
    Methods EM algorithm was employed to solve the maximum likelihood estimation (MLE) of parameters of general linear model (GLM) based on Gamma distribution with interval data,then univariate and multivariate anal yses were applied to explore factors that influen-cing SARS incubation period.
    方法 采用EM算法求解基于含区间数据Gamma分布的广义线性模型(GLM)参数的极大似然估计(MLE)。 采用所建立的GLM方法对含区间数据SARS潜伏期进行单因子、多因子分析。
短句来源
    Objective To develop a method to estimate the two parameters of Gamma distribution with interval data and conduct that to estimate the length of incubation period of severe acute respiratory syndrome (SARS).
    目的建立含区间数据Gamma分布的参数估计方法,并用于SARS潜伏期的推算。
短句来源
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  “gamma分布”译为未确定词的双语例句
    Purpose To develop a method to make facto ri al analysis of dependent variable with interval data fitted to Gamma distrib ut ion and conduct it to explore factors that influencing the incubation period of severe acute respiratory syndrome (SARS).
    目的 建立含区间数据Gamma分布变量的因子分析方法,并应用于SARS潜伏期的影响因素分析。
短句来源
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  gamma distribution
A new estimate of shape parameter in the family of Gamma distribution
      
In this paper, a new estimator of the shape parameter in the family of Gamma distribution is constructed by using the moment idea, and it is proved that this estimator is strongly consistent and asymptotically normal.
      
Application of the theory of truncated probability distributions to studying minimal river runoff: Normal and gamma distribution
      
Content subscribing mechanism in P2P streaming based on gamma distribution prediction
      
From statistical regress, it was also found that the DSD follows the Gamma distribution best in most cases.
      
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Purpose To develop a method to make facto ri al analysis of dependent variable with interval data fitted to Gamma distrib ut ion and conduct it to explore factors that influencing the incubation period of severe acute respiratory syndrome (SARS). Methods EM algorithm was employed to solve the maximum likelihood estimation (MLE) of parameters of general linear model (GLM) based on Gamma distribution with interval data,then univariate and multivariate anal yses were applied to explore factors that influen-cing...

Purpose To develop a method to make facto ri al analysis of dependent variable with interval data fitted to Gamma distrib ut ion and conduct it to explore factors that influencing the incubation period of severe acute respiratory syndrome (SARS). Methods EM algorithm was employed to solve the maximum likelihood estimation (MLE) of parameters of general linear model (GLM) based on Gamma distribution with interval data,then univariate and multivariate anal yses were applied to explore factors that influen-cing SARS incubation period. Results We obtained MLE,the approximate standard error of each parameter of GLM,and the results of significant test of GLM.The results from factorial analysis of SARS incubation period showed that both contact patte rn and epidemic area had significant impacts on the incubation period;older pati ents had a longer incubation period;and the incubation period of the medical per sonnel was obviously shorter than that of the non-medical personnel. Conclusions MLE based on EM algorithm is robust for par ameter estimation of GLM based on Gamma distribution with interval data.GLM meth od can be used to explore factors that influencing SARS incubation period with i nterval data.

目的 建立含区间数据Gamma分布变量的因子分析方法,并应用于SARS潜伏期的影响因素分析。方法 采用EM算法求解基于含区间数据Gamma分布的广义线性模型(GLM)参数的极大似然估计(MLE)。采用所建立的GLM方法对含区间数据SARS潜伏期进行单因子、多因子分析。结果 采用EM算法,得到了含区间数据Gamma分布GLM中各个参数的MLE、近似的标准误、以及参数和模型的显著性检验结果。用于SARS潜伏期影响因素分析,发现不同接触方式、不同疫区对SARS潜伏期有明显影响;随着年龄增长,SARS潜伏期均值呈上升趋势;医务人员的潜伏期明显短于非医务人员。结论 基于EM算法的MLE方法对于含区间数据Gamma分布GLM参数的估计是强健的,GLM方法可以用于含区间数据SARS潜伏期的影响因素分析。

Objective To develop a method to estimate the two parameters of Gamma distribution with interval data and conduct that to estimate the length of incubation period of severe acute respiratory syndrome (SARS).Methods EM algorithm was employed to construct an iterative formula for solving the maximum likelihood estimation (MLE) of parameters of Gamma distribution with interval data,whereby we can estimate the distribution parameters of SARS incubation period with interval data.Results The two parameters of Gamma...

Objective To develop a method to estimate the two parameters of Gamma distribution with interval data and conduct that to estimate the length of incubation period of severe acute respiratory syndrome (SARS).Methods EM algorithm was employed to construct an iterative formula for solving the maximum likelihood estimation (MLE) of parameters of Gamma distribution with interval data,whereby we can estimate the distribution parameters of SARS incubation period with interval data.Results The two parameters of Gamma distribution with interval data can be estimated by MLE based on EM algorithm,whereby the estimation of the mean can be obtained.Meanwhile,the standard error and the confidence interval for each parameter also can be calculated by using the asymptotic property of MLE.The data of SARS outbreak from mainland of China in 2003 analyzed by above method found that SARS incubation period had a Gamma (2.10,2.33) distribution; MLE of the mean and variance of SARS incubation period was 4.89 days (95% confidence interval 4.43-5.35) and 11.40 days 2,respectively; therefore 95% of SARS patients would experience the onset of symptoms within 11.42 days.Conclusion MLE based on EM algorithm is robust for parameter estimation of Gamma distribution with interval data and can be employed to estimate the distribution parameters of SARS incubation period with interval data.

目的建立含区间数据Gamma分布的参数估计方法,并用于SARS潜伏期的推算。方法采用EM算法构造出求解含区间数据Gamma分布参数极大似然估计的迭代公式,并应用于SARS潜伏期分布的拟合。结果基于EM算法的极大似然估计方法可以计算出含区间数据Gamma分布的两个参数,从而得到均值估计。同时,还可以根据极大似然估计的渐近性质,计算出估计量的标准误及各参数的置信区间。用于中国内地SARS爆发资料分析,发现SARS潜伏期服从Gamma(2.1,2.33)分布;潜伏期均值和方差的极大似然估计值分别为4.89天(95%CI4.43~5.35)和11.40天2;95%的病人感染SARS-CoV后将在11.42天内发病。结论基于EM算法的极大似然估计方法对于含区间数据Gamma分布参数的估计是强健的,可以用于含区间数据SARS潜伏期的精确估计。

 
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