This paper discusses the solution of nonlinear integral equations with noisy integral kernels as they appear in nonparametric instrumental regression. We propose a regularized Newton-type iteration and establish convergence and convergence rate results. A particular emphasis is on instrumental regression models where the usual conditional mean assumption is replaced by a stronger independence assumption. We demonstrate for the case of a binary instrument that our approach allows the correct estimation of regression functions which are not identifiable with the standard model. This is illustrated in computed examples with simulated data.
The computationally efficient method of fitness function evaluation (criterion for chromosomes selection) in genetic algorithms (GA) is discussed in this paper. This method may be used if a single gene modifies chromosome.
Steiner's problem in graphs is solved for the computing optimization. Population is represented as a weighted graph. Vertices of that graph represent chromosomes, edges represent the computational cost of selection criteria recurrent calculation. The GA application for identification of regression models assumes (a) gene is a regressor;
(b) chromosome is the set of regressors in single regression model (subset of all candidates);
(c) population — set of regression models (subset of all possible models); (d) selection criteria — residual sum of squares (RSS); (e) the chromosome modification by modification of one gene corresponds to the forward selection and backward elimination methods of variables (regressors) selection.
The number of papers addressing the forecasting of the infectious disease morbidity is rapidly growing due to accumulation of available statistical data. This article surveys the major approaches for the short-term and the long-term morbidity forecasting. Their limitations and the practical application possibilities are pointed out. The paper presents the conventional time series analysis methods — regression and autoregressive models; machine learning-based approaches — Bayesian networks and artificial neural networks; case-based reasoning; filtration-based techniques. The most known mathematical models of infectious diseases are mentioned: classical equation-based models (deterministic and stochastic), modern simulation models (network and agent-based).