In: Biometrics, 2005, vol. 61, no. 2, p. 507-514
Variable selection is an essential part of any statistical analysis and yet has been somewhat neglected in the context of longitudinal data analysis. In this paper we propose a generalized version of Mallows's Cp (GCp) suitable for use with both parametric and nonparametric models. GCp provides an estimate of a measure of model's adequacy for prediction. We examine its performance with popular...
|
In: Journal of Health Economics, 2006, vol. 25, no. 2, p. 198-213
In this paper robust statistical procedures are presented for the analysis of skewed and heavy-tailed outcomes as they typically occur in health care data. The new estimators and test statistics are extensions of classical maximum likelihood techniques for generalized linear models. In contrast to their classical counterparts, the new robust techniques show lower variability and excellent...
|
In: Journal of the American Statistical Association, 2001, vol. 96, no. 455, p. 1022-1030
By starting from a natural class of robust estimators for generalized linear models based on the notion of quasi-likelihood, we de¯ne robust deviances that can be used for stepwise model selection as in the classical framework. We derive the asymptotic distribution of tests based on robust deviances and we investigate the stability of their asymptotic level under contamination. The binomial and...
|
In: Statistical modelling, 2011, vol. 11, no. 3, p. 237-252
We adapt Breiman’s (1995) nonnegative garrote method to perform variable selection in nonparametric additive models. The technique avoids methods of testing for which no general reliable distributional theory is available. In addition it removes the need for a full search of all possible models, something which is computationally intensive, especially when the number of variables is...
|
In: Journal of Empirical Finance, 2007, vol. 14, no. 4, p. 546-563
We propose Indirect Robust Generalized Method of Moments (IRGMM), a simulationbased estimation methodology, to model short-term interest rate processes. The primary advantage of IRGMM relative to classical estimators of the continuous-time short-rate diffusion processes is that it corrects both the errors due to discretization and the errors due to model misspecification. We apply this approach...
|
|
|
In: Metron, 2004, vol. 62, no. 2, p. 161-184
We propose robust counterparts to tests of equal forecast accuracy such as those proposed by Diebold and Mariano (1995) and West (1996). We illustrate the robustness problem and evaluate the size and the power properties of the classical and robust tests under various types of deviations from model assumptions. The new robust test has a correct size and larger power across a wide spectrum of...
|
In: Annals of the Institute of Statistical Mathematics, 2008, vol. 60, no. 1, p. 205-224
We obtain marginal tail area approximations for the one-dimensional test statistic based on the appropriate component of the M-estimate for both standardized and Studentized versions which are needed for tests and confidence intervals. The result is proved under conditions which allow the application to finite sample situations such as the bootstrap and involves a careful discretization with...
|
In: Journal of the American statistical association, 1996, vol. 91, no. 434, p. 666-673
Saddlepoint approximations of marginal densities and tail probabilities of general nonlinear statistics are derived. They are based on the expansion of the statistic up to the second order. Their accuracy is shown in a variety of examples, including logit and probit models and rank estimators for regression.
|
In: Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2004, vol. 66, no. 4, p. 893–908
Generalized Linear Latent Variable Models (GLLVM), as defined in Bartholomew and Knott (1999) enable modelling of relationships between manifest and latent variables. They extend structural equation modelling techniques, which are powerful tools in the social sciences. However, because of the complexity of the log-likelihood function of a GLLVM, an approximation such as numerical integration must...
|
