为什么叫非参数统计？

非参数统计是统计学的一个分支，基本要义是不要求知道总体的分布。但为什么叫非参数统计呢？
不估计参数吗？不是的。
不解其义。

我觉得是这样的：参数统计是在已知总体分布类型，比较一些参数（比如常用来比较均值，方差等参数），而由于有些数据总计分布不知道，无法运用一般统计原理进行分析，所以研究出了一种用位置变量（常用秩）来讨论分析问题，那么这就与总体的参数没有太多关系，所以称作非参数统计。（研究位置变量）

lowess 曲线估计（非参还是半参）就没有估计方程和参数，就一条线

胖胖小龟宝 发表于 2014-2-23 18:16
我觉得是这样的：参数统计是在已知总体分布类型，比较一些参数（比如常用来比较均值，方差等参数），而由于 ...

谢谢，思考中……

谢谢两位朋友，确实，非参数统计不再对估计参数，哪怕是非参数回归，仍然不再估计参数，而是估计曲线，

谢谢评分

gyelle 发表于 2014-2-23 20:00
谢谢评分

不客气

胖胖小龟宝 发表于 2014-2-23 17:53
我觉得是这样的：参数统计是在已知总体分布类型，比较一些参数（比如常用来比较均值，方差等参数），而由于 ...

还是有缺陷：非参数统计不再估计参数，但还是可以检验参数的，即非参数检验这一块。

In statistics, the term non-parametric statistics has at least two different meanings:

• The first meaning of non-parametric covers techniques that do not rely on data belonging to any particular distribution. These include, among others:
  

• Distribution free methods, which do not rely on assumptions that the data are drawn from a given probability distribution. As such it is the opposite of parametric statistics. It includes non-parametric descriptive statistics, statistical models, inference and statistical tests.
• Non-parametric statistics (in the sense of a statistic over data, which is defined to be a function on a sample that has no dependency on a parameter), whose interpretation does not depend on the population fitting any parameterised distributions. Order statistics, which are based on the ranks of observations, are one example of such statistics and these play a central role in many non-parametric approaches.
  

2.The second meaning of non-parametric covers techniques that do not assume that the structure of a model is fixed. Typically, the model grows in size to accommodate the complexity of the data. In these techniques, individual variables are typically assumed to belong to parametric distributions, and assumptions about the types of connections among variables are also made. These techniques include, among others:

• Non-parametric regression, which refers to modeling where the structure of the relationship between variables is treated non-parametrically, but where nevertheless there may be parametric assumptions about the distribution of model residuals.
• Non-parametric hierarchical Bayesian models, such as models based on the Dirichlet process, which allow the number of latent variables to grow as necessary to fit the data, but where individual variables still follow parametric distributions and even the process controlling the rate of growth of latent variables follows a parametric distribution.
  

耕耘使者 发表于 2014-2-23 21:45
还是有缺陷：非参数统计不再估计参数，但还是可以检验参数的，即非参数检验这一块。

检验的不是分布么？