654 – Risk Model Selection and Extreme Values
Temporal Prediction Models from Marginal and Small Data Signal: The Otolith Data Example
Cynthia Jones
Old Dominion University
Rajan Lamichhane
Old Dominion University
Rajan Lamichhane
Old Dominion University
Stochastic processes have applications in many areas such as oceanography and engineering. Special classes of such processes deal with time series of sparse data. Studies in such cases focus in the analysis, construction and prediction in parametric models. Here, we assume several non-linear time series with additive noise components, and the model �tting is proposed in two stages. The �rst stage identi�es the density using all the clusters information, without specifying any prior knowledge of the underlying distribution function of the time series. The effect of covariates is controlled by �tting the linear regression model with serially correlated errors. In the second stage, we partition the time series into consecutive non-overlapping intervals of quasi stationary increments where the coef�cients shift from one stable regression relationship to a different one using a breakpoints detection algorithm. These breakpoints are estimated by minimizing the likelihood from the residuals. We approach time series prediction through the mixture distribution of combined error components. Parameter estimation of mixture distribution is done by using the EM algorithm. We apply the method to �sh otolith data influenced by various environmental conditions and get estimation of parameters for the model.
