Application of parameter estimation and hypothesis test for a generalized gamma distribution

D. Nicholson
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引用次数: 1

Abstract

This paper develops maximum likelihood (ML) estimation procedures for the parameters of a generalized Gamma probability density function. In addition, the likelihood ratio conditioned on the ML estimates of the process parameters is given. A special case arising in the analysis of carbon monoxide pollution data is discussed in detail and the performance of the binary hypothesis test functioning as a pollution forecasting algorithm is reported.
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广义伽玛分布参数估计和假设检验的应用
本文提出了广义伽玛概率密度函数参数的极大似然估计方法。此外,给出了以过程参数的ML估计为条件的似然比。详细讨论了一氧化碳污染数据分析中的一个特例,并报道了二值假设检验作为一种污染预测算法的性能。
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