Robust mixing.
Robust Parametric Estimation of Branching Processes with a Random Number of Ancestors
2000 Mathematics Subject Classification: 60J80.The paper deals with a robust parametric estimation in branching processes {Zt(n)} having a random number of ancestors Z0(n) as both n and t tend to infinity (and thus Z0(n) in some sense). The offspring distribution is considered to belong to a discrete analogue of the exponential family – the class of the power series offspring distributions. Robust estimators, based on one and several sample paths, are proposed and studied for all values of the offspring...
Robust pole placement for second-order systems: an LMI approach
Based on recently developed sufficient conditions for stability of polynomial matrices, an LMI technique is described to perform robust pole placement by proportional-derivative feedback on second-order linear systems affected by polytopic or norm-bounded uncertainty. As illustrated by several numerical examples, at the core of the approach is the choice of a nominal, or central quadratic polynomial matrix.
Robust quasi-linear system identification
Robust recursive estimation of GARCH models
The robust recursive algorithm for the parameter estimation and the volatility prediction in GARCH models is suggested. It seems to be useful for various financial time series, in particular for (high-frequency) log returns contaminated by additive outliers. The proposed procedure can be effective in the risk control and regulation when the prediction of volatility is the main concern since it is capable to distinguish and correct outlaid bursts of volatility. This conclusion is demonstrated by...
Robust Selection Procedures Based on Vector Ranks.
Robust time series analysis: a survey
Robustez cualitativa en regiones de confianza.
In this paper, Hampel's concept of qualitative robustness (1968, 1971) is adapted to the problem of estimation by confidence regions. The basic idea is to consider the confidence regions as generalized estimates taking values in the space of compact sets endowed with the Hausdorff metric.In section 3, the qualitative robustness is analyzed in five particular cases, which include confidence regions and tolerance intervals of common use. Section 4 is devoted to discussion and comments.
Robustness of estimation of first-order autoregressive model under contaminated uniform white noise
The first-order autoregressive model with uniform innovations is considered. In this paper, we study the bias-robustness and MSE-robustness of modified maximum likelihood estimator of parameter of the model against departures from distribution of white noise. We used the generalized Beta distribution to describe these departures.
Robustness of sample mean and sample median under restrictions on outliers
Robustness of tests based on spacings in the exponential model
Robustness of the best linear unbiased estimator and predictor in linear regression models
If is shown that in linear regression models we do not make a great mistake if we substitute some sufficiently precise approximations for the unknown covariance matrix and covariance vector in the expressions for computation of the best linear unbiased estimator and predictor.
Robustness of the sample correlation -- the bivariate lognormal case.
Robustness optimal spring balance weighing designs for estimation total weight
In this paper we develop the theory of spring balance weighing designs with non-positive correlated errors for that the lower bound of the variance of estimated total weight is attained.
Robustness regions for measures of risk aggregation
One of risk measures’ key purposes is to consistently rank and distinguish between different risk profiles. From a practical perspective, a risk measure should also be robust, that is, insensitive to small perturbations in input assumptions. It is known in the literature [14, 39], that strong assumptions on the risk measure’s ability to distinguish between risks may lead to a lack of robustness. We address the trade-off between robustness and consistent risk ranking by specifying the regions in...
Robustness to non-normality of common tests for the many-sample location problem.
Robustness to non-normality of various tests for the one-sample location problem.
Robustness to the Unavailability of Data in Block Designs.
Role of Experimental Randomization in Bayesian Statistics: An Asymptotic Theory for a Single Bayesian.
Rolling-ball method for estimating the boundary of the support of a point-process intensity