Robust Estimation of the Structural Errors-in-Variables Model.
Robust estimations in classical regression models versus robust estimations in fuzzy regression models
In this paper are presented two robust estimators of unknown fuzzy parameters in the fuzzy regression model and investigated the relationship between these robust estimators in the classical regression model and in the fuzzy regression model.
Robust estimators and their relations
Robust Kalman filter and its application in time series analysis
Robust median estimator for generalized linear models with binary responses
The paper investigates generalized linear models (GLM's) with binary responses such as the logistic, probit, log-log, complementary log-log, scobit and power logit models. It introduces a median estimator of the underlying structural parameters of these models based on statistically smoothed binary responses. Consistency and asymptotic normality of this estimator are proved. Examples of derivation of the asymptotic covariance matrix under the above mentioned models are presented. Finally some comments...
Robust m-estimator of parameters in variance components model
It is shown that a method of robust estimation in a two way crossed classification mixed model, recently proposed by Bednarski and Zontek (1996), can be extended to a more general case of variance components model with commutative a covariance matrices.
Robust methods in exponential smoothing
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 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 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.