Recursive estimation in autoregressive models with additive outliers
Regression quantiles and trimmed least squares estimator under a general design
Robust Bayesian estimation in a normal model with asymmetric loss function
The problem of robust Bayesian estimation in a normal model with asymmetric loss function (LINEX) is considered. Some uncertainty about the prior is assumed by introducing two classes of priors. The most robust and conditional Γ-minimax estimators are constructed. The situations when those estimators coincide are presented.
Robust estimation and forecasting for beta-mixed hierarchical models of grouped binary data.
The paper focuses on robust estimation and forecasting techniques for grouped binary data with misclassified responses. It is assumed that the data are described by the beta-mixed hierarchical model (the beta-binomial or the beta-logistic), while the misclassifications are caused by the stochastic additive distorsions of binary observations. For these models, the effect of ignoring the misclassifications is evaluated and expressions for the biases of the method-of-moments estimators and maximum...
Robust estimation based on spacings in weighted exponential models
Using Zieliński's (1977, 1983) formalization of robustness Błażej (2007) obtained uniformly most bias-robust estimates (UMBREs) of the scale parameter for some statistical models (including the exponential model), in a class of linear functions of order statistics, when violations of the models are generated by weight functions. In this paper the UMBRE of the scale parameter, based on spacings, in two weighted exponential models is derived. Extensions of results of Bartoszewicz (1986, 1987) are...
Robust estimation in a linear regression model
Robust estimation in capital asset pricing model.
Robust estimation of the scale and weighted distributions
The concept of robustness given by Zieliński (1977) is considered in cases where violations of models are generated by weight functions. Uniformly most bias-robust estimates of the scale parameter, based on order statistics, are obtained for some statistical models. Extensions of results of Zieliński (1983) and Bartoszewicz (1986) are given.
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 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 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.