Model selection criteria often arise by constructing unbiased or approximately unbiased estimators of measures known as expected overall discrepancies (Linhart & Zucchini, 1986, p. 19). Such measures quantify the disparity between the true model (i.e., the model which generated the observed data) and a fitted candidate model. For linear regression with normally distributed error terms, the “cor...

Smoothing splines are a popular method for performing nonparametric regression. Most important in the implementation of this method is the choice of the smoothing parameter. This article provides a simulation study of several smoothing parameter selection methods, including two so{called risk estimation methods. To the best of the author's knowledge, the empirical performances of these two risk...

Although there are disadvantages associated with model building procedures such as backward, forward and stepwise procedures (e.g. multiple testing, arbitrary significance level used in dropping or acquiring variables), many analysts use these procedures and are not aware that alternative modeling selection methods exist. This paper focuses on model selection using the Akaike Information Criter...

For the problem of estimation of Money demand model of Pakistan, money supply (M1) shows heteroscedasticity of the unknown form. For estimation of such model we compare two adaptive estimators with ordinary least squares estimator and show the attractive performance of the adaptive estimators, namely, nonparametric kernel estimator and nearest neighbour regression estimator. These comparisons a...

We present a statistical perspective on boosting. Special emphasis is given to estimating potentially complex parametric or nonparametric models, including generalized linear and additive models as well as regression models for survival analysis. Concepts of degrees of freedom and corresponding Akaike or Bayesian information criteria, particularly useful for regularization and variable selectio...

Dependent Variable: Y Method: Least Squares Date: 05/23/00 Time: 05:55 Sample: 1 33 Included observations: 33 Variable Coefficient Std. Error t-Statistic Prob. C 102192.4 12799.83 7.983891 0.0000 N -9074.674 2052.674 -4.420904 0.0001 P 0.354668 0.072681 4.879810 0.0000 I 1.287923 0.543294 2.370584 0.0246 R-squared 0.618154 Mean dependent var 125634.6 Adjusted R-squared 0.578653 S.D. dependent v...

This paper describes the LOESS procedure which is a new procedure in SAS/STAT R software for performing local regression. Features of this procedure are outlined and a brief description of the fitting method is given. Examples are given illustrating the use of this procedure in obtaining fitted surfaces as well as prediction confidence limits for both univariate and multivariate regressor data....

It is a common situation that the failure rate function has a bathtub shape for many mechanical and electronic components. A simple model based on adding two Burr XII distributions is presented for modeling this type data. The graphical estimation on probability paper is illustrated, and examples of its usage are presented. The Akaike Information Criterion was used for judging the adequacy of t...

The optimum s-gradient method for minimizing a positive definite quadratic function f(x) on E has long been known to converge for s > 1 . For these £ the author studies the directions from which the iterates x. approach their limit, and extends to s > 1 a theory proved by Akaike for s = 1 . It is shown that f (x. ) can never converge to its minimum value faster than linearly, except in degenera...