Timeline for Does the invariance property hold for consistent estimators within an indicator function? [closed]
Current License: CC BY-SA 4.0
7 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| 2 days ago | history | closed |
Snoop Leucippus Harish Chandra Rajpoot Kurt G. amWhy |
Needs details or clarity | |
| Jul 3 at 11:49 | review | Close votes | |||
| 2 days ago | |||||
| Jul 2 at 23:35 | comment | added | JerBear | For this line, I'm replacing $f(X_{n})$ with $f(c) + o_{P}(1)$, assuming $f(c)$ is continuous at $c$. This I can do assuming that the sequence of random variables $X_{n}$ converge in probability to a constant. I think this comment actually helped me get closer to an answer, which I can post tomorrow God's will (still waiting on the shrimp scampi ðŸ˜) | |
| Jul 2 at 23:25 | comment | added | Henry | Now I do not understand $\Delta\left(P_{n} \leq f\left(X_{n}\right)\right) = \Delta\left(P_{n} \leq f\left(c\right) + o_{P}(1) \right)$ is saying. In any case, what happens if you remove the randomness with $f(x)=x$, $X_n=c+\frac1n$, $P_n=c+\frac2n$ or with $f(x)=x$, $X_n=c+\frac2n$, $P_n=c+\frac1n$ ? | |
| Jul 2 at 23:11 | comment | added | JerBear | @henry, sorry, I corrected the post. Thank you for pointing that out! | |
| Jul 2 at 23:11 | history | edited | JerBear | CC BY-SA 4.0 |
deleted 78 characters in body
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| Jul 2 at 22:09 | history | asked | JerBear | CC BY-SA 4.0 |