L-optimal designs in the trigonometric regression model on the full circle

2007 ◽  
Vol 40 (2) ◽  
pp. 158-168
Author(s):  
P. V. Shpilev
Keyword(s):  
Regression Model ◽  
Optimal Designs ◽  
Full Circle ◽  
Trigonometric Regression

A comparative study of univariate models for container throughput forecasting of major ports in Asia

2021 ◽  
pp. 147509022110236
Author(s):  
Juan Huang ◽  
Ching-Wu Chu ◽  
Hsiu-Li Hsu
Keyword(s):  
Regression Model ◽  
Moving Average ◽  
Forecasting Model ◽  
Short Term ◽  
Autoregressive Integrated Moving Average ◽  
Port Authorities ◽  
Grey Forecasting ◽  
Grey Forecasting Model ◽  
Short Term Forecasting ◽  
Trigonometric Regression

This study aims to make comparisons on different univariate forecasting methods and provides a more accurate short-term forecasting model on the container throughput for rendering a reference to relevant authorities. We collected monthly data regarding container throughput volumes for three major ports in Asia, Shanghai, Singapore, and Busan Ports. Six different univariate methods, including the grey forecasting model, the hybrid grey forecasting model, the multiplicative decomposition model, the trigonometric regression model, the regression model with seasonal dummy variables, and the seasonal autoregressive integrated moving average (SARIMA) model, were used. We found that the hybrid grey forecasting model outperforms the other univariate models. This study’s findings can provide a more accurate short-term forecasting model for container throughput to create a reference for port authorities.

On the Choice of a Prior for Bayesian D-Optimal Designs for the Logistic Regression Model with a Single Predictor

2014 ◽  
Vol 43 (7) ◽  
pp. 1811-1824 ◽  
Author(s):  
Haftom T. Abebe ◽  
Frans E. S. Tan ◽  
Gerard J. P. Van Breukelen ◽  
Jan Serroyen ◽  
Martijn P. F. Berger
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Optimal Designs ◽  
Single Predictor
Sign in / Sign up

Export Citation Format

Share Document