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- Huang, Lei; Jiang, Hui; Wang, Huixia
- Computational statistics & data analysis 2019 v.134 pp. 110-122
- autocorrelation; models; time series analysis
- ... Partial-linear single-index models have been widely studied and applied, but their current applications to time series modeling still need some strong and inappropriate assumptions. A novel method which relaxes those assumptions is proposed. It extends the applicability of partial-linear single-index models to time series modeling, taking both lag variables and autocorrelated errors into considera ...
- Singh, Rakhi; Mukhopadhyay, Siuli
- Computational statistics & data analysis 2019 v.134 pp. 157-170
- Bayesian theory; autocorrelation; disease surveillance; sequence analysis; time series analysis; variance covariance matrix
- ... Exact D-optimal Bayesian designs for time series experiments are discussed in this article. This work is motivated by an RNA sequencing experiment and two disease surveillance studies, where the response is count type and has a correlated structure over time points. The conditional distribution of the count responses given a weakly stationary latent process is assumed to follow a log-linear model. ...