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natski

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Any thoughts would be appreciated!

natski

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- Thread starter natski
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In summary, the mode of a probability distribution cannot be derived exactly from the characteristic function. Although moments can be computed using a power series expansion, there is no equation for computing the mode. One suggestion is to differentiate the inverse Fourier transform, but this approach may only work under certain assumptions about the pdf and characteristic function.

- #1

natski

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Any thoughts would be appreciated!

natski

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- #2

mathman

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- #3

natski

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Natski

- #4

bpet

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To solve df/dx=0, what if you differentiate the inverse Fourier transform (with suitable assumptions on the pdf and c.f.) - if p(t) is the c.f. then the modes would be the zero-amplitude frequencies of (t*p(t)) ?

Calculating the mode of a distribution from the characteristic function refers to finding the most frequently occurring value in a set of data based on the characteristic function, which is a mathematical function that describes the probability distribution of a random variable.

The mode is an important measure of central tendency in a distribution because it represents the most common or typical value. It can provide valuable insights into the underlying patterns and trends in the data.

The mode can be calculated by finding the value that corresponds to the highest point on the characteristic function curve. This can be done using mathematical formulas or by graphing the characteristic function and visually identifying the peak.

Yes, the mode can be calculated for any type of distribution, including normal, binomial, and exponential distributions. However, for continuous distributions, the mode may not have a well-defined value and may need to be estimated using numerical methods.

The mode differs from other measures of central tendency such as the mean and median because it is not affected by extreme values or outliers in the data. It only takes into account the most frequently occurring value, making it a useful measure for skewed or non-normal distributions.

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