Minimum Value of Absolute Deviation

In summary, the conversation is discussing how to prove that the quantity S = \sum_{i=1}^{n}|X_{i} - a| is minimum when a is the median of the X_{i}'s. The participants suggest re-ordering the sample in increasing order and working by contradiction to prove this.
  • #1
maverick280857
1,789
4
Hi,

How can I rigorously prove that the quantity

[tex]S = \sum_{i=1}^{n}|X_{i} - a|[/tex]

(where [itex]X_{1},\ldots,X_{n}[/itex] is a random sample and a is some real number) is minimum when a is the median of the [itex]X_{i}[/itex]'s?

Thanks.
 
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  • #2
Ok I think I got it. If a is the median, then there are just as many numbers less than it than there are greater than it...but how do I write the median in terms of the random sample?
 
  • #3
maverick280857 said:
Ok I think I got it. If a is the median, then there are just as many numbers less than it than there are greater than it...but how do I write the median in terms of the random sample?

There's no simple way to write it like you can with, say, the mean. What you can do is re-order the sample in increasing order, such that [itex]X_1 \leq \ldots \leq X_{n/2} \leq a \leq X_{n/2 + 1} \leq \ldots \leq X_n[/itex]. For the actual proof, you might try working by contradiction: assume some other value of a results in the lowest value for the sum, and then show that you can construct an even lower value by moving a towards the median.
 

What is the definition of Minimum Value of Absolute Deviation?

The Minimum Value of Absolute Deviation is a statistical measure that calculates the average distance between a set of data points and their mean or median. It is also known as the Mean Absolute Deviation (MAD) and is typically used to measure the variability or spread of a data set.

How is Minimum Value of Absolute Deviation calculated?

To calculate the Minimum Value of Absolute Deviation, follow these steps:

  • Find the mean or median of the data set.
  • Find the absolute difference between each data point and the mean or median.
  • Add all of the absolute differences together.
  • Divide the sum by the total number of data points.

The result is the Minimum Value of Absolute Deviation.

What is the difference between Minimum Value of Absolute Deviation and Standard Deviation?

The Minimum Value of Absolute Deviation and Standard Deviation are both measures of variability in a data set. The main difference is that Standard Deviation takes into account the squared differences of each data point, while Minimum Value of Absolute Deviation uses the absolute value of the differences. This means that Standard Deviation is more sensitive to extreme values, while Minimum Value of Absolute Deviation gives equal weight to all data points.

When is Minimum Value of Absolute Deviation used?

Minimum Value of Absolute Deviation is used to measure the variability or spread of a data set. It is often used as a more robust alternative to Standard Deviation, especially when the data set has extreme values or is not normally distributed. It is also commonly used in finance and economics to measure risk and volatility.

What are the limitations of Minimum Value of Absolute Deviation?

One of the limitations of Minimum Value of Absolute Deviation is that it does not take into account the direction of the differences between data points and the mean or median. This means that positive and negative differences are treated the same, which may not accurately reflect the data. Additionally, Minimum Value of Absolute Deviation is more difficult to use in mathematical calculations compared to Standard Deviation, as it does not have the same mathematical properties.

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