REAGAN Estimates of a Quasilinear Regression
We study Design and Analysis of non-linear in parameters smooth Quasilinear Regression Model (abbreviated as Q-model) which admits in every iteration almost the same type of analysis as linear regression for simultaneous iterative estimation of both the mean and variance of observations. Q-models are more flexible in applications than Linear models. We prove local convergence of iterations θs, s ∈ Z, in probability and the asymptotic normality of θˆ = lims→∞ θs in iteration procedure called REweighted Algorithm of GAuss-Newton and illustrate its broad use by many appropriate applications.
КЛЮЧЕВЫЕ СЛОВА: Gauss-Newton algorithm, iterations, convergence in probability, asymptotic normality, maximum likelihood, exponential families