Mapping the agenda-setting theory, priming and the spiral of silence in Twitter accounts of political parties

Duatis, Marc Blasco; Coenders, Germà; Sáez, Marc; García, Núria Fernández; Cunha, Isabel Ferin

doi:10.1504/ijwbc.2019.098696

Cited by 6 publications

References 40 publications

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Multivariate cumulative sum control chart for compositional data with known and estimated process parameters

Imran

Sun

Zaidi

et al. 2022

Quality & Reliability Eng

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This article uses the classic multivariate cumulative sum (MCUSUM$\mathrm{MCUSUM}$) chart scheme proposed by Crossier (1988) to present a new modified MCUSUM$\mathrm{MCUSUM}$ chart for compositional data (CoDa$\mathrm{CoDa}$). For this purpose, the data are first transformed using isometric log‐ratio (ilr$\operatorname{ilr}$) coordinates representation to eliminate the constant sum constraint of CoDa$\mathrm{CoDa}$. The MCUSUM$\mathrm{MCUSUM}$‐CoDa$\mathrm{CoDa}$ control chart has been defined along with the performance measures of the proposed chart using the average run length (ARL$\mathrm{ARL}$). Besides, the Markov chain method has been used to study the ARL$\mathrm{ARL}$ performance of the proposed chart. Assuming that the ilr$\operatorname{ilr}$ transformed data are normally distributed, the proposed MCUSUM$\mathrm{MCUSUM}$‐CoDa$\mathrm{CoDa}$ charts have been compared with existing competitors such as T2$T^2$‐CoDa$\mathrm{CoDa}$ and MEWMA$\mathrm{MEWMA}$‐CoDa$\mathrm{CoDa}$ charts. The comparison shows that the proposed chart has better performance than the T2$T^2$‐CoDa$\mathrm{CoDa}$ control charts, while the performance of the proposed chart is comparable with the MEWMA$\mathrm{MEWMA}$‐CoDa$\mathrm{CoDa}$ chart. The effect of the estimated mean vector and variance‐co‐variance matrix on run‐length characteristics of the proposed MCUSUM$\mathrm{MCUSUM}$‐CoDa$\mathrm{CoDa}$ control chart has also been studied in this paper. For the ARL$\mathrm{ARL}$ performance of MCUSUM$\mathrm{MCUSUM}$‐CoDa$\mathrm{CoDa}$ with estimated parameters Monte Carlo simulation has been adopted. The effect of the number of variables p$p$, sample size n$n$, and subgroup size m$m$ has also been studied on the data's upper control limit (UCL$\mathrm{UCL}$) and ARL$\mathrm{ARL}$. In the end, two illustrative examples of the particle size distribution of plants and production of muesli are provided to represent the practical implementation of the MCUSUM$\mathrm{MCUSUM}$‐CoDa$\mathrm{CoDa}$ chart.

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