Weighted Importance in sampled data statistics

In summary, the conversation discusses how to appropriately modify first and second order statistics when weighting a given sample. The speaker mentions using a weight vector to measure the reliability of each sample, and asks if the mean and variance can be calculated using this method. The responder then provides the equations for calculating a weighted mean and sample variance.
  • #1
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154
2
I was just wondering how exactly to appropriately modify the 1st and 2nd order stats when you want to weight a given sample more heavily. If [itex] \vec{X} [/itex] is my vector of N samples and I have a weight vector [itex] \vec{W} [/itex] of the same dimension, which ideally is measuring the reliability of each sample from 0 to 1. Could I calculate mean and variance using...

[itex]
\vec{Y}_i= \vec{X}_i\vec{W}_i
\\

\mu_{x-weighted} = E[\vec{Y}]
\\

\sigma_{x-weighted} = E[(\vec{Y}-E[\vec{Y}])^2]
\\

[/itex]

Let me know what you think. Thanks.

EDIT: [itex] \LaTeX [/itex] mods...
 
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  • #2
The weighted mean is given by...

[tex]\mu_w = \frac{\sum x_iw_i}{\sum w_i}[/tex]

The weighted sample variance is...

[tex]\sigma^2_w = \frac{\sum w_i(x_i - \mu_w)^2}{\sum w_i}[/tex]
 
  • #3
Hey thanks!
 

1. What is weighted importance in sampled data statistics?

In statistics, weighted importance refers to the practice of assigning different weights to observations in a data set based on their relative importance or prevalence in the population being studied. This allows for more accurate analysis and conclusions to be drawn from the data.

2. How are weights determined in weighted importance?

Weights can be determined in a variety of ways, depending on the specific data and research question. In some cases, weights may be assigned based on demographic factors, such as age or gender, while in others they may be based on the frequency of a particular attribute or behavior in the population.

3. What are the advantages of using weighted importance in statistics?

Weighted importance allows for a more accurate representation of the population being studied, as it takes into account the varying levels of importance or prevalence of different observations. This can lead to more precise estimates and better understanding of relationships between variables.

4. Are there any limitations to using weighted importance?

One potential limitation of weighted importance is that it relies on assumptions about the population being studied and the factors used to assign weights. If these assumptions are incorrect, it can lead to biased results. Additionally, the process of assigning weights can be subjective, and different researchers may assign weights differently.

5. How can weighted importance be applied in real-world situations?

Weighted importance is commonly used in market research, political polling, and other fields where accurate representation of a population is crucial. It can also be applied in healthcare research, where certain patient groups may be more prevalent and therefore warrant higher weights in the data analysis.

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