Qualitative Robustness for Stochastic Processes
نویسندگان
چکیده
منابع مشابه
Qualitative Robustness for General Stochastic Processes
Abreviated Title: Qualitative Robustness SUMMARY In this paper we generalize Hampel's definition of robustness and IT-robustness of a sequence of estimators to the case of non i.i.d. stochastic processes, using appropriate metrics on the space of finite and infinite dimensional samples. We also present a different approach to qualitative robust-ness based on uniform insensitivity of the sequenc...
متن کاملRobustness for Point Processes
We consider robustness for estimation of point processes parameters. An influence functional measures the effect of contamination on estimators. We derive some of its properties, and use it to propose an M-estimator alternative to the MLE for the inhomogeneous Poisson process case. We also show the consistency and asymptotic normality of this estimator.
متن کاملA Useful Family of Stochastic Processes for Modeling Shape Diffusions
One of the new area of research emerging in the field of statistics is the shape analysis. Shape is defined as all the geometrical information of an object whose location, scale and orientation is not of interest. Diffusion in shape analysis can be studied via either perturbation of the key coordinates identifying the initial object or random evolution of the shape itself. Reviewing the f...
متن کاملStochastic Processes ( Fall 2014 ) Spectral representations and ergodic theorems for stationary stochastic processes Stationary stochastic processes
A stochastic process X is strongly stationary if its fdds are invariant under time shifts, that is, for any (finite) n, for any t0 and for all t1, ..., tn ∈ T , (Xt1 , ..., Xtn) and (Xt1+t0 , ..., Xtn+t0) have the same distribution. A stochastic process X is weakly stationary if its mean function is constant and its covariance function is invariant under time shifts. That is, for all t ∈ T , E(...
متن کاملA robustness result for stochastic control
The solution of a stochastic control problem depends on the underlying model. The actual real world model may not be known precisely and so one solves the problem for a hypothetical model, that is in general different but close to the real one; the optimal (or nearly optimal) control of the hypothetical model is then used as solution for the real problem. In this paper we assume that, what is n...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: The Annals of Statistics
سال: 1987
ISSN: 0090-5364
DOI: 10.1214/aos/1176350506