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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In: Journal de la Société Française de Statistique, 2006, vol. 147, no. 2, p. 73-75
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This article reports on the results of an intercultural card sorting experiment with 104 students in Beijing and Cambridge to identify similarities and differences in the interpretation of visual business communication formats. The results show that English and Chinese strategy students differ dramatically in terms of their similarity and grouping decisions of business diagrams. The results...
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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...
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We model demand for different classes of antibiotics used for respiratory infections in outpatient care using a linear approximate almost ideal demand system approach. We compute elasticities to socioeconomic determinants of consumption and own- and cross- price elasticities between different groups of antibiotics. We find significant elasticities between newer/more expensive generations and...
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In: Energy Economics, 2007, vol. 29, no. 1, p. 64-78
This paper studies the cost structure of gas distribution utilities in Switzerland. Three stochastic frontier models are applied to a panel of 26 companies operating from 1996 to 2000. Efficiency is assumed to be constant over time. The analysis highlights the importance of output characteristics such as customer density and network size. The results suggest that the utilities could slightly...
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In: Journal of Econometrics, 2001, vol. 101, no. 1, p. 37-69
The local robustness properties of Generalized Method of Moments (GMM) estimators and of a broad class of GMM based tests are investigated in a unified framework. GMM statistics are shown to have bounded influence if and only if the function defining the orthogonality restrictions imposed on the underlying model is bounded. Since in many applications this function is unbounded, it is useful to...
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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...
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