这个只能不断尝试啊，没有什么一定管用的办法。首先试试只用一部分数据估计。如果不管用哪部分数据都不行那就有可能是你模型有问题。不然的话可能就是数据的问题。但是不看到具体情况没法具体判断的。

考虑两个问题，也许能解决。
1、因变量分类是否恰当。
2、不同自变量标准差相差太远。
试试重新编码。
Here is my paraphrasing of an argument Scott Long has made concerning convergence problems:

Scaling of variables. Scaling can be important. The larger the ratio between the largest standard deviation and the smallest standard deviation, the more problems you will have with numerical methods. For example, if you have income measured in dollars, it may have a very large standard deviation relative to other variables. Recoding income to thousands of dollars may solve the problem. Long says that, in his experience, problems are much more likely when the ratio between the largest and smallest standard deviations exceeds 10. (You may want to rescale for presentation purposes anyway, e.g. the effect of 1 dollar of income may be extremely small and have to be reported to several decimal places; coding income in thousands of dollars
may make your tables look better.)

楼主，请问你解决了吗？