Timeline for Why does the three points follow by making the two assumptions about the conditioned intensity function?
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17 events
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| Jan 20, 2021 at 10:55 | comment | added | Yasmin | @LSpice: Sure, the paper I'm reading is in this link arxiv.org/pdf/1806.00221.pdf, and the proof, or the proposition, is in page 9. | |
| Jan 20, 2021 at 10:50 | comment | added | Yasmin | By the way, would it be considered wrong to say that, $F \to 1$ for $t \to \infty$, as $e^{-\int_{t_n}^{t}\lambda^*(s)ds} \to 0$ for $t \to \infty$? Because does it not do that? | |
| Jan 20, 2021 at 10:39 | comment | added | Yasmin | Yemon Choi: Thank you that makes sense. I'll oplaod my question in math math.stackexchange.com. | |
| Jan 20, 2021 at 1:02 | comment | added | Yemon Choi | Yasmin: given that some function $F(t)$ has the form $1- e^{-h(t)}$ where $h(t)\geq 0$ for all $t$, it should be clear that $0\leq F(t)\leq 1$ for all $t$. This seems to give point 1 and I think that points 2 and 3 will follow from similar reasons. If further explanation is required then I think math.stackexchange.com might be a more appropriate site to get help with such arguments | |
| Jan 20, 2021 at 0:43 | review | Close votes | |||
| Jan 26, 2021 at 20:37 | |||||
| Jan 20, 2021 at 0:26 | comment | added | LSpice | Rather than just answering what the paper's about, why not also mention the specific paper? | |
| Jan 19, 2021 at 23:48 | comment | added | Yasmin | Moreover, this proof's objective is to show that $F$ is in fact a cumulative distribution function (which thus is proofed by giving the three points). I hope this made it more clear. | |
| Jan 19, 2021 at 23:42 | comment | added | Yasmin | I hope this is the answer you are looking for. The paper is about temporal point processes, more specifically, evolutionary temporal point processes (In other words, the present event (point) is dependent of the previous event(s)). This is a proof I'm trying to understand. I just can't seem to find the correct arguments to state why these two assumptions imply the three points. | |
| Jan 19, 2021 at 22:35 | comment | added | Dieter Kadelka | @Yemon Choi: Of course number 1 can be answered without knowing anything about the underlying model. But I think it's always good to define anything which is not standard. I still do not know: Is the underlying model a point process or is it reliability theory or something else? Maybe you can answer this immediately, I can't. And maybe it's unimportant in this context. | |
| Jan 19, 2021 at 22:15 | comment | added | Yemon Choi | For a start, if you have tried to derive point 1 from the given information, at which point did you get stuck? | |
| Jan 19, 2021 at 22:15 | comment | added | Yemon Choi | Yasmin: in its current form, the question might be more suitable for stats.stackexchange.com just because it is using terminology/definitions/conventions that are tacitly understood in that setting. | |
| Jan 19, 2021 at 22:12 | comment | added | Yemon Choi | @DieterKadelka Doesn't point number 1 follow from the stated definitions/assumptions without needing further information on the underlying process? | |
| Jan 19, 2021 at 21:49 | comment | added | Dieter Kadelka | Without knowing the paper you start with who shall answer your question? Is there a underlying stochastic process? What? | |
| Jan 19, 2021 at 20:47 | history | edited | Yasmin | CC BY-SA 4.0 |
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| Jan 19, 2021 at 19:10 | history | edited | Malkoun | CC BY-SA 4.0 |
added 15 characters in body
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| Jan 19, 2021 at 19:02 | review | First posts | |||
| Jan 20, 2021 at 0:26 | |||||
| Jan 19, 2021 at 19:01 | history | asked | Yasmin | CC BY-SA 4.0 |