如何进行数据拟合以找到给定数据的分布 [英] How to do data fitting to find the distribution of given data

问题描述

我需要进行数据拟合以找到给定数据的分布.

I need to do data fitting to find the distribution of a given data.

我需要找到发行版的pdf函数.

I need to find the pdf function of the distribution.

我可以在matlab和python中使用数据拟合函数.

I can use data fitting functions in matlab and python.

它看起来像是截断的伽马.

It looks like a truncated gamma.

但是，如何找到分布的参数?

But, how to find the parameters of the distribution ?

如果数据不能很好地适应截断的伽玛?

What if the data cannot fit the truncated gamma well ?

QQ图(四分位数)表明它不适合截断 伽玛.

The QQ-plot (qunatile-quantile) show that it is not a good fit for truncated gamma.

如何查找分布参数，例如alpha(形状)，beta(比例) 截断的伽玛?

How to find the distribution parameters such as alpha (shape), beta (scale) for the truncated gamma ?

如果无法在此处进行数据拟合，那么我还可以使用哪些其他方法?

If data fitting cannot work here, what other methods I can use for that ?

任何帮助将不胜感激.

推荐答案

查看 allfitdist 在Matlab中.

Check out allfitdist in Matlab.

或者，考虑使用特殊软件包，例如ExpertFit或EasyFit. JMP统计软件还具有相当易于使用的分布拟合选项.所有这些都会评估拟合优度标准，例如 Cramer- von Mises 并记录似然估计.

Alternatively, consider specialty packages such as ExpertFit or EasyFit. The JMP statistical software also has a fairly easy to use distribution fitting option. All of these will evaluate goodness-of-fit criteria such as Cramer-von Mises and log likelihood estimates.

选择了分布的函数形式后，参数值通常由最大似然估计器或时刻方法.

Once you've picked the functional form of the distribution, parameter values are usually estimated by maximum likelihood estimators or method of moments.

如果您打算在某种模拟中使用结果，则可以考虑仅引导示例而不是分布拟合.模拟的另一种选择是运行一个设计好的实验，在该实验中，您可以改变分布的选择，并在担心太多合适的分布之前，先看看替代方法是否会对您的结果产生重大影响.

If you're planning on using the results in a simulation of some sort, you might consider just bootstrapping your sample rather than distribution fitting. Yet another option if simulating would be to run a designed experiment where you vary the distribution choice and see if alternatives have a significant impact on your results before worrying too much about fitting just the right distribution.

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