Timeline for How to test if these two samples are from the same distribution? [closed]
Current License: CC BY-SA 3.0
21 events
| when toggle format | what | by | license | comment | |
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| Oct 26, 2018 at 12:50 | history | closed |
mdewey kjetil b halvorsen♦ Sycorax♦ Michael R. Chernick COOLSerdash |
Needs details or clarity | |
| Oct 25, 2018 at 11:40 | review | Close votes | |||
| Oct 26, 2018 at 12:50 | |||||
| Oct 25, 2018 at 8:47 | comment | added | Sextus Empiricus | 1. The diagrams should be explained more clearly. What are the x and y axis, what are the colour intensity of the dots? (shushwap lake?) 2. The situation should be sketched with more context. There seems to be clustering along two lines but can we explain this somehow? Such explanation can help to devise a model that allows a more powerful test. | |
| Oct 25, 2018 at 8:00 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| Sep 22, 2018 at 14:25 | answer | added | Anatoly Alekseev | timeline score: 0 | |
| Dec 8, 2017 at 20:28 | history | post merged (destination) | |||
| Dec 8, 2017 at 20:01 | answer | added | coreydevinanderson | timeline score: 1 | |
| Dec 7, 2017 at 0:02 | comment | added | monotonic | How do I move this to chat? Or can I have your email? | |
| Dec 7, 2017 at 0:01 | comment | added | monotonic | I have two twitter samples in two periods, how do I know if they're from the same distribution? I.e. I want to see if there is structural changes people travel. | |
| Dec 6, 2017 at 16:18 | comment | added | coreydevinanderson | Tools for modeling inhomogeneous linear point patterns are limited. Your best bet is the R package "spatstat". Is that what you wanted to do....test whether tweet locations are dependent on intensity of hotels? I can explain to you how to do this in R. I would like to formally answer the question, but you have to tell me what you want to test exactly. If there is more back and forth clarification required, we need to do this via chat/message rather than via commenting. | |
| Dec 6, 2017 at 5:54 | answer | added | knk | timeline score: 0 | |
| Dec 6, 2017 at 5:07 | comment | added | monotonic | can you give examples of both? | |
| Dec 6, 2017 at 4:49 | comment | added | Dale C | Do you have an assumption about the distributions they follow, or is this non-parametric? | |
| Dec 6, 2017 at 4:42 | history | asked | monotonic | CC BY-SA 3.0 | |
| Dec 6, 2017 at 3:01 | comment | added | monotonic | Is there a simple way I can do this in python? test against inhomogeneous poisson? | |
| Dec 6, 2017 at 2:26 | comment | added | coreydevinanderson | This looks to me like a point pattern on a linear network (roads?). Do you want to test whether the tweets are dependent on the intensity of hotels? Usually we start by testing against a simple null model, such as a homogeneous Poisson point process (on a linear network), and if we can reject that we start to explore other models, such as an inhomogeneous Poisson point process model, where variation in intensity may be driven by covariates, such as variation in the the intensity of hotels. It could also be some sort of cluster process or multiype/multivariate process. Tell me more! | |
| Dec 5, 2017 at 10:39 | comment | added | monotonic | you can now see those | |
| Dec 5, 2017 at 4:04 | comment | added | coreydevinanderson | I think you have point pattern data. You want to understand whether the pattern of tweets with a certain hash tag represent a realization from the same underlying distribution. There is not a simple answer to this question, but I think I can point you in the right direction, but you will need to update your question with the information in your comment so that the admin can reopen it. Provide as much detail as possible (and perhaps even a map, if feasible). | |
| Dec 4, 2017 at 9:15 | comment | added | monotonic | Can you give a example of both? | |
| Dec 4, 2017 at 9:07 | comment | added | monotonic | The data I have are scraped from a specific hashtag, containing user id, caption, location, time. It's tourists' data so it's very random, concentrated around tourist attractions. | |
| Dec 3, 2017 at 15:21 | comment | added | coreydevinanderson | You should provide more information about the process underlying the tweets to determine if there is a test that would work. Do you view the tweets as coming from a fixed location with some sort of measurement (boy vs. girl, type of tweet, time of day, etc.)? Or is there a stochastic aspect to the spatial distribution of tweets such that they could be viewed as a realization of a point process (or marked point process)? |