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问题描述
我有一个时间序列x_0 ... x_t.我想计算数据的指数加权方差.那就是:
I have a time series x_0 ... x_t. I would like to compute the exponentially weighted variance of the data. That is:
V = SUM{w_i*(x_i - x_bar)^2, i=1 to T} where SUM{w_i} = 1 and x_bar=SUM{w_i*x_i}ref: en.wikipedia/wiki/Weighted_mean#Weighted_sample_variance
目标是从根本上权衡那些可以使时间倒退得更短的观察结果.这实现起来非常简单,但是我想使用尽可能多的内置函数.有人知道R中对应什么吗?
The goal is to basically weight observations that are further back in time less. This is very simple to implement but I would like to use as much built in funcitonality as possible. Does anyone know what this corresponds to in R?
谢谢
推荐答案R提供加权平均值.实际上,?weighted.mean显示了以下示例:
R provides weighted mean. In fact, ?weighted.mean shows this example:
## GPA from Siegel 1994 wt <- c(5, 5, 4, 1)/15 x <- c(3.7,3.3,3.5,2.8) xm <- weighted.mean(x, wt)又一步:
v <- sum(wt * (x - xm)^2)更多推荐
计算加权平均值和标准差
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