Point estimate from multiple sampling distributions

In summary, the speaker is seeking help with summarizing multiple distributions of a parameter of interest, θ, from two experimental groups. They have tried averaging the means and medians of each distribution, but are unsure of the best approach. They are considering using convolution to obtain a single point estimate and confidence interval.
  • #1
capy_bara
1
0
Dear all, I hope someone can help me.

I have two experimental groups, A (n=5) and B (n=8) containing biological samples. The samples are used to estimate my parameter of interest, θ. I do this with Markov-chain Monte-Carlo, which gives me a posterior distribution of θ for each of my samples. The distributions look approximately normal but are skewed a bit to the right. All values for θ are positive.
I wonder how I can now report single estimates (and confidence intervals) for θ in groups A and B, respectively.

Averaging the means of each distribution in one group gives me very different results than averaging the medians of all distributions. I also tried combining all distributions from one group and then taking the mean or the median. How can I find out which is the best way to summarize all distributions within one group to get a single point estimate?

I hope my problem is clear, I would appreciate any help.
Many thanks in advance,
capy_bara
 
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  • #2
For each sample in A and B, you get a distribution of (possible) θ, and you want a single number and confidence interval for θ in A and B?

How can I find out which is the best way to summarize all distributions within one group to get a single point estimate?
Convolution, if nothing simpler works. This allows to get your point estimate and confidence level in a natural way.
 

1. What is a point estimate?

A point estimate is a single value that is used to approximate the true value of a population parameter. It is calculated using sample data and is often used when it is not feasible or practical to collect data from the entire population.

2. How is a point estimate calculated?

A point estimate is calculated by taking a specific statistic from a sample, such as the mean or proportion, and using it as an estimate for the corresponding parameter in the population. For example, the sample mean can be used as a point estimate for the population mean.

3. What are multiple sampling distributions?

Multiple sampling distributions refer to the distribution of sample statistics, such as means or proportions, that are calculated from multiple samples of the same size from the same population. These distributions can be used to estimate the variability of the population parameter of interest.

4. How can point estimates be derived from multiple sampling distributions?

Point estimates can be derived from multiple sampling distributions by taking the mean or median of the sample statistics. This can provide a more accurate estimate of the population parameter, as it takes into account the variability among multiple samples.

5. What is the significance of point estimates from multiple sampling distributions?

Point estimates from multiple sampling distributions can provide valuable information about the population parameter of interest, such as its variability and potential bias. They can also be used to make inferences about the population and to test hypotheses about the true value of the parameter.

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