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Description: |
The main discussion will be on distributions and their characterization in terms of the moments. There are distributions which are unique (M-determinate) and others which are non-unique (M-indeterminate). When analyzing specific stochastic model we clarify why in some cases a distribution is M-determinate and in other cases it is M-indeterminate. Besides classical criteria we present some recent developments and use them to study Box-Cox functional transformations of random data coming from observations of random variables or of stochastic processes. All statements and criteria will be well illustrated by examples involving popular distributions such as Normal, Skew-Normal, Log-normal, Skew-Log-Normal, Exponential, Gamma, Poisson, IG, etc. Several facts will be reported, some of them are not so well-known, they are surprising and even shocking. It will be shown that the moment determinacy of the distributions is essential in inference problems. Some challenging open questions will be outlined. The speaker hopes the talk to be of interest to professionals in statistics, probability and mathematics but also to Doctoral and Master students in these areas.
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Date: |
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Start Time: |
11:45 |
Speaker: |
Jordan Stoyanov (Newcastle Univ., UK)
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Institution: |
Newcastle University, U.K.
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Place: |
Room 5.5
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Research Groups: |
-Probability and Statistics
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See more:
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