Jackknifing in Nonlinear Regression

On Jackknifing Kernel Regression Function

"Estimation of the value of a regression function at a point of continuity using a kernel-type estimator is discussed and improvements of the technique by a generalized jackknife estimator are presented. It is shown that the generalized jackknife technique produces estimators with faster bias rates. In a small example it is investigated, if the generalized jackknife method works for all choices...

Inference in Nonlinear Regression

This paper considers inference in a standard nonlinear regression model. We show that the model is non-regular in the sense that test statistics of interest exhibit a discontinuity in their limit distribution as a function of a parameter in the model. The discontinuity occurs when the coe¢ cient of a nonlinear regressor is zero. This paper establishes the asymptotic distributions of the least s...

Nonlinear regression

Nonlinear Structure in Regression Residuals

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...

Predictions in Nonlinear Regression Modelsf

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.