Stata分位回归

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急！！！问下各位在Stata中使用ologit或者oprobit命令进行分位回归的命令是怎样的，以下结果是什么意思
. ologit m26b m39 m52 m1 a12 gender age m23 m1 m25 u48a
note: m1 omitted because of collinearity
Iteration 0:   log likelihood = -10.050677  
Iteration 1:   log likelihood = -7.0168028  
Iteration 2:   log likelihood = -4.9737289  
Iteration 3:   log likelihood = -3.8226942  
Iteration 4:   log likelihood = -1.8149996  
Iteration 5:   log likelihood =          0 (not concave)
Iteration 6:   log likelihood =          0 (not concave)
Iteration 7:   log likelihood =          0 (not concave)
Iteration 8:   log likelihood =          0 (not concave)
Iteration 9:   log likelihood =          0 (not concave)
Iteration 10:  log likelihood =          0 (not concave)
Iteration 11:  log likelihood =          0 (not concave)
Iteration 12:  log likelihood =          0 (not concave)
Iteration 13:  log likelihood =          0 (not concave)
Iteration 14:  log likelihood =          0 (not concave)
Iteration 15:  log likelihood =          0 (not concave)
Iteration 16:  log likelihood =          0 (not concave)
Iteration 17:  log likelihood =          0 (not concave)
Iteration 18:  log likelihood =          0 (not concave)
Iteration 19:  log likelihood =          0 (not concave)
Iteration 20:  log likelihood =          0 (not concave)
Iteration 21:  log likelihood =          0 (not concave)
Iteration 22:  log likelihood =          0 (not concave)
Iteration 23:  log likelihood =          0 (not concave)
Iteration 24:  log likelihood =          0 (not concave)
Iteration 25:  log likelihood =          0 (not concave)
Iteration 26:  log likelihood =          0 (not concave)
more--

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关键词：Stata tata 分位回归 Collinearity Likelihood because

该命令不是分位数回归，是有序的离散选择模型，结果是最大似然回归迭代过程，显示不收敛。

从理论上说，ologit或oprobit（离散因变量）的“分位数回归”，没有什么意义。