H. Zheng, S.R. Kulkarni, H.V. Poor, "A sequential predictor retraining algorithm and its appli- cation to market prediction," Annals of Operations Research, Vol. 208, No. 1, pp. 209-225, 2013.
The performance of the designed sequential predictor (mathbb {Q}) is compared to the best predictor (mathbb {P}) in the class (mathbb {M}).
However, the sequential predictor performance generally depends on the predictor set (mathbb {M}) class "complexity" or richness, which quantifies the class type, size, and statistical regression between observations [19,20,23,29].
Ultimately, the sequential predictor performance depends on the predictor set (mathbb {M}) "complexity" or richness, which quantifies the class type, size, and statistical regression between observations [19,20,23,29].
In broad terms, a sequential predictor is either fitted to the observations series, i.e. curve fitting or the observation generating stochastic distribution, i.e. density fitting to estimate future observations.
A model with the highest log marginal likelihood (or the highest posterior probability, assuming equal priors on structure) is the best sequential predictor of the data D. For any given gene, the probability that this gene is a member of cluster c could be calculated by [ 43]: where j* is the parent instantiate of the network structure for gene expression pattern c and k = 1.
Hierarchical regression is also called sequential regression; predictors are entered into the equation in the order specified by the researcher.
The objective is to find a kind of separation (actually a sequential design) between predictor and control pole placement.
A spatial structure of stations is defined by the sequential introduction of predictors in the model.
Models were constructed by sequential addition of predictors and comparison of models using the Akaike information criteria and the likelihood-ratio chi-squared value.
In a sequential prediction framework, these techniques represent different parametrised predictor classes.
