beta分布alpha和beta的Stata识别
Stata identification of beta distribution alpha and beta
当数据加载到 Stata 时出现变量名,显然我可以制作直方图来查看分布:
histogram *variablename*
但是如果分布看起来是 beta 分布,如何找到 alpha 和 beta?
来自 SSC 的 betafit
就是这样做的。在 Stata 中输入
. search beta
会指向这个(实际上还有很多)。
示例:
. clear
. set seed 2803
. set obs 100
number of observations (_N) was 0, now 100
. gen y = rbeta(2, 2)
. ssc install betafit
checking betafit consistency and verifying not already installed...
installing into !!!
installation complete.
. betafit y
Iteration 0: log likelihood = 12.898998
Iteration 1: log likelihood = 13.093709
Iteration 2: log likelihood = 13.094329
Iteration 3: log likelihood = 13.094329
ML fit of beta (alpha, beta) Number of obs = 100
Wald chi2(0) = .
Log likelihood = 13.094329 Prob > chi2 = .
------------------------------------------------------------------------------
y | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
alpha |
_cons | 1.945302 .2606829 7.46 0.000 1.434373 2.456232
-------------+----------------------------------------------------------------
beta |
_cons | 2.11004 .2854789 7.39 0.000 1.550512 2.669568
------------------------------------------------------------------------------
当数据加载到 Stata 时出现变量名,显然我可以制作直方图来查看分布:
histogram *variablename*
但是如果分布看起来是 beta 分布,如何找到 alpha 和 beta?
betafit
就是这样做的。在 Stata 中输入
. search beta
会指向这个(实际上还有很多)。
示例:
. clear
. set seed 2803
. set obs 100
number of observations (_N) was 0, now 100
. gen y = rbeta(2, 2)
. ssc install betafit
checking betafit consistency and verifying not already installed...
installing into !!!
installation complete.
. betafit y
Iteration 0: log likelihood = 12.898998
Iteration 1: log likelihood = 13.093709
Iteration 2: log likelihood = 13.094329
Iteration 3: log likelihood = 13.094329
ML fit of beta (alpha, beta) Number of obs = 100
Wald chi2(0) = .
Log likelihood = 13.094329 Prob > chi2 = .
------------------------------------------------------------------------------
y | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
alpha |
_cons | 1.945302 .2606829 7.46 0.000 1.434373 2.456232
-------------+----------------------------------------------------------------
beta |
_cons | 2.11004 .2854789 7.39 0.000 1.550512 2.669568
------------------------------------------------------------------------------