In: Psychometrika, 2002, vol. 67, no. 1, p. 21-32
|
In: ASTIN Bulletin, 1992, vol. 22, no. 1, p. 33-49
|
In: Journal of the american statistical association, 2010, vol. 105, no. 490, p. 703–712
We develop infinitesimally robust statistical procedures for the general diffusion processes. We first prove the existence and uniqueness of the times-series influence function of conditionally unbiased M-estimators for ergodic and stationary diffusions, under weak conditions on the (martingale) estimating function used. We then characterize the robustness of M-estimators for diffusions and...
|
In: Insurance: Mathematics and Economics, 2008, vol. 42, no. 2, p. 746-762
We consider the classical risk model and carry out a sensitivity and robustness analysis of finite-time ruin probabilities. We provide algorithms to compute the related influence functions. We also prove the weak convergence of a sequence of empirical finite-time ruin probabilities starting from zero initial reserve toward a Gaussian random variable. We define the concepts of reliable finite-time...
|
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...
|
In: Journal of the American Statistical Association, 2005, vol. 100, no. 470, p. 628-641
This paper studies the local robustness of estimators and tests for the conditional location and scale parameters in a strictly stationary time series model. We first derive optimal bounded-influence estimators for such settings under a conditionally Gaussian reference model. Based on these results, optimal bounded-influence versions of the classical likelihood-based tests for parametric...
|
