Abstract
We present a new model in Social Networks which allows experts in this field to analyze social networks. In this paper, a joint random effect linear model for analysing longitudinal inflated [0,1]-support and inflated count response variables, where there is the possibility of non-ignorable missing values for inflated [0,1]-support response variable, has been presented. Considering the posterior distribution of unknowns given all available information. A Monte Carlo EM algorithm is used for estimating the posterior distribution of the parameters. A sensitivity of the results to the assumptions is also investigated the perturbation from missing at random to not missing at random. Influence of small perturbation of these elements on posterior displacement is also studied. Finally, for showing the applicability of the proposed model, results from analyzing Enron email dataset and student activity and profile dataset are presented. Also, a new statistical monitoring to study the longitudinal social network datasets via considering attributes which are important in various applications is provided. For this purpose, a complete definition of responsiveness rate in social networks as a [0,1]-support variable has been presented.
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Notes
\(P_{Y_{(i,j)t}}^{INFBE_{k,l}}\), a probability measure on the measurable space \(({\Omega }, \mathfrak {B})\), is absolutely continuous with respect to the σ −finite measure μ = μL + δk + δl, where μL indicates the Lebesgue measure and δc is a point mass at c.
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Samani, E.B., Tabrizi, E. Joint Linear Modeling of Mixed Data and Its Application to Email Analysis. Sankhya B 85, 175–209 (2023). https://doi.org/10.1007/s13571-023-00304-w
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DOI: https://doi.org/10.1007/s13571-023-00304-w
Keywords
- Joint linear model
- inflated beta distributions
- inflated power series distribution
- sensitivity analysis
- responsiveness rate
- social network.