|
|||||
|
|
A Novel Estimation Approach for Mixture Transition Distribution Model in High-Order Markov Chains |
|
| Authors: | D. G. Chen |
| Affiliation: | 1. Department of Mathematics and Statistics, Agricultural Experiment Station , South Dakota State University , Brookings , South Dakota , USA;2. Department of Surgery, Sanford School of Medicine , University of South Dakota , Sioux Falls , South Dakota , USA |
| Abstract: | A transformation is proposed to convert the nonlinear constraints of the parameters in the mixture transition distribution (MTD) model into box-constraints. The proposed transformation removes the difficulties associated with the maximum likelihood estimation (MLE) process in the MTD modeling so that the MLEs of the parameters can be easily obtained via a hybrid algorithm from the evolutionary algorithms and/or quasi-Newton algorithms for global optimization. Simulation studies are conducted to demonstrate MTD modeling by the proposed novel approach through a global search algorithm in R environment. Finally, the proposed approach is used for the MTD modelings of three real data sets. |
| Keywords: | Genetic algorithms High-order temporal dependence Markov chains Maximum likelihood estimation Mixture transition distribution |