This article considers the problem of model averaging for regression models that can be nonlinear in their parameters and variables. We consider a (NMA) framework propose weight-choosing criterion, information criterion (NIC). show up to constant, NIC is an asymptotically unbiased estimator risk function under settings with some mild assumptions. also prove optimality convergence weights. Monte...

in the present study, the mechanical behavior of aa5052 aluminum alloy during cold deformation and subsequent isothermal annealing in a temperature range of 225-300oc was investigated using the uniaxial tensile test data. it is found that by increasing the annealing time and temperature the material yield strength is decreased. the microstructural investigations of the annealed samples show tha...

Phase space reconstruction is investigated as a diagnostic tool for determining the structure of detected nonlinear processes in regression residuals. Empirical evidence supporting this approach is provided using simulations from an Ikeda mapping and the S&P 500. Results in the form of phase portraits (e.g., scatter plots of reconstructed dynamical systems) provide qualitative information to di...

We introduce, for the first time, a new class of Birnbaum–Saunders nonlinear regression models potentially useful in lifetime data analysis. The class generalizes the regression model described by Rieck and Nedelman [1991, A log-linear model for the Birnbaum–Saunders distribution, Technometrics, 33, 51–60]. We discuss maximum likelihood estimation for the parameters of the model, and derive clo...

For many applications of nonlinear regression, theory does not guide the model building process by suggesting a relevant functional form. This research develops a new technique for use in this situation. Called STAT-ANN, the new method provides modelling flexibility similar to a neural network but provides a statistical basis for model selection, interpretation, and validation by following a tw...

Diierent predictors and their approximators in nonlinear prediction regression models are studied. The minimal value of the mean squared error (MSE) is derived. Some approximate formulae for the MSE of ordinary and weighted least squares predictors are given.