Name
Title | ||
|---|---|---|
Modeling Phone Call Durations via Switching Poisson Processes with Applications in Mental Health |
Abstract | ||
|---|---|---|
This work models phone call durations via switching Poisson point processes. This kind of processes is composed by two intertwined intensity functions: one models the start of a call, whereas the other one models when the call ends. Thus, the call duration is obtained from the inverse of the intensity function of finishing a call. Additionally, to model the circadian rhythm present in human behavior, we shall use a (pos-itive) truncated Fourier series as the parametric form of the intensities. Finally, the maximum likelihood estimates of the intensity functions are obtained using a trust region method and the performance is evaluated on synthetic and real data, showing good results. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/MLSP49062.2020.9231856 | 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP) |
Keywords | DocType | ISSN |
Intensity function,maximum likelihood (ML) estimation,point processes,switching Poissing process,trust region method | Conference | 1551-2541 |
ISBN | Citations | PageRank |
978-1-7281-6663-6 | 0 | 0.34 |
References | Authors | |
5 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pablo Bonilla-Escribano | 1 | 0 | 0.34 |
| David Ramírez | 2 | 3 | 1.04 |
| Antonio Artés-Rodríguez | 3 | 206 | 34.76 |