network profile
| bio | website | |
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| location | ||
| age | ||
| visits | member for | 2 years |
| seen | May 2 '11 at 12:09 | |
| stats | profile views | 6 |
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May 1 |
awarded | Scholar |
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May 1 |
accepted | Conditions for Central Limit Theorem for dependent sequences |
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May 1 |
comment |
Conditions for Central Limit Theorem for dependent sequences OK. Now I understood why it is ergodic. Trivially all shift invariant subsets have either probability zero or one. |
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May 1 |
comment |
Conditions for Central Limit Theorem for dependent sequences Sorry, I was wrong again. Mixing is stronger. Then it might be ergodic even if it's not mixing. |
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May 1 |
awarded | Supporter |
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May 1 |
comment |
Conditions for Central Limit Theorem for dependent sequences First of all thanks for your long and careful answer, I really appreciate that. As for the example, it is very interesting, but I am struggling to understand why the process is ergodic: the notion of ergodicity I have in mind, coupled with stationarity, implies strong mixing, i.e. asymptotic independence of the terms of the sequence, which in this example seem perfectly dependent. |
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May 1 |
comment |
Conditions for Central Limit Theorem for dependent sequences OK. Right, we must ensure that V stays finite. Is there any counter-example where V goes to infinity for a stationary ergodic process? |
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May 1 |
awarded | Student |
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May 1 |
asked | Conditions for Central Limit Theorem for dependent sequences |
