名义尺度
- 网络nominal scale
-
经典的聚类分析技术如系统聚类法和K-means等主要是处理间隔尺度的变量,而对于名义尺度变量则不适合。
Traditional clustering techniques such as systemic clustering and K-means clustering adapt to interval scale variants , but not suit nominal scale variants .
-
基于名义尺度的空间自相关指数测度方法研究
Measurement Method of Spatial Autocorrelation Index Based on Nominal Scale
-
两个名义尺度总体参数的估计与检验
Estimation and Test of Parameters for Two Nominal Scale Populations
-
基于名义尺度栅格数据空间自相关测度初探
Preliminary Study on Spatial Autocorrelation Measure Based on Nominal Scale Grid Data
-
一种针对名义尺度变量的优化聚类算法
An Optimization-based Clustering Algorithm for Nominal Scale Variants
-
具有名义尺度的两个总体概率分布相等检验的功效
The Effectiveness of Test for the Equality of the Probability Distribution of Two Populations with Nominal Scales
-
本文研究了一个名义尺度总体分布参数的经验贝叶斯估计与线性经验贝叶斯估计问题。
The paper discuss empirical Bayes estimation and linear empirical Bayes estimation of the parameters of one nominal scale population .
-
空间数据根据其参考标准可将其分为间隔尺度数据二比例尺度数据、顺序尺度数据和名义尺度数据。
Spatial datas could be classified as interval datas , ratio datas , order datas and nominal datas according reference standard .
-
本文以名义尺度下的栅格数据作为研究对象,通过定义空间自邻接指数提出一种空间自相关测度算法,并将其应用到土地利用数据分析中得到了较好的试验结果。
While defining the spatial auto-relation index and an algorithm for spatial autocorrelation measurement is propsed , and the fair results are obtained in the experiment with the land use data .
-
运用空间数据尺度有时会遇到麻烦的概念和逻辑问题,必须仔细严格划定数据的名义尺度、有序尺度、间隔尺度和比率尺度,才会避免自我矛盾。
Sometimes there are problems with the concept and logic in using the spatial data scales . It will be possible to avoid the confusion with each other , only if the nominal , ordinal , interval and ratio scales of the data are carefully classified and defined .
-
古典经济学中的古典两分命题认为,实际经济与名义经济互不相关:以货币为单位的名义变量的同尺度变化,只能影响价格和工资等名义变量,而对实际经济变量没有影响。
In the opinions of the classical dichotomy , the real economy is uncorrelation to the nominal economy . When the nominal variables that measured with currency changed in the same scale , they only have effects on price and wage , but have no effects on real economy variables .