In: International encyclopedia of statistical science, 2011, p. 1240-1242
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In: Journal of the American statistical association, 2011, vol. 106, no. 493, p. 147-156
We develop second order hypothesis testing procedures in functional measurement error models for small or moderate sample sizes, where the classical first order asymptotic analysis often fails to provide accurate results. In functional models no distributional assumptions are made on the unobservable covariates and this leads to semiparametric models. Our testing procedure is derived using...
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In: Journal of multivariate analysis, 2009, vol. 100, no. 9, p. 2126-2136
In the framework of generalized linear models, the nonrobustness of classical estimators and tests for the parameters is a well known problem and alternative methods have been proposed in the literature. These methods are robust and can cope with deviations from the assumed distribution. However, they are based on ¯rst order asymptotic theory and their accuracy in moderate to small samples...
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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.
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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...
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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...
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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...
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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...
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In: The Annals of Statistics, 2003, vol. 31, no. 4, p. 1154-1169
We consider multidimensional M-functional parameters defined by expectations of score functions associated with multivariate M-estimators and tests for hypotheses concerning multidimensional smooth functions of these parameters. We propose a test statistic suggested by the exponent in the saddlepoint approximation to the density of the function of the M-estimates. This statistic is analogous to...
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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...
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