A Kind of Statistics Data Processing with Indicators in an Autocorrelation Network

Abstract:

Efforts to develop data processing or some variable evaluation of the components autocorrelation network or interpersonal relationship network have been hampered by many obstacles. One important reason is the lag of autocorrelation network model (ANM) development. Autocorrelation network models are used to deal with the data with autocorrelation network data. However, some data are correlated with its lag station, so its necessary to introduce the time series in the ANM. The network autocorrelation model with lag and auto-correlated indicators or variables is put forward based on the expansion of the existing social network effect model. The estimation method of the network autocorrelation model is illustrated; the time series stationarity, the consistency and effectiveness of the estimation method are discussed. Also the application of the model is discussed, especially the practical significance on the forecasting and controlling.

• Periodical: Applied Mechanics and Materials (Volumes 401-403)
• Pages: 1278-1281
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.401-403.1278
• Online since: September 2013
• Authors: Hui Liu, Lei Li, Chuan Mei Wang
• Keywords: Determining the Order, Network Autocorrelation Model, Statistics Data Processing, Two Stage Least Squares (2LS)

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