Displaying similar documents to “Estimating the contamination level of data in the framework of linear regression analysis.”

Robust Parametric Estimation of Branching Processes with a Random Number of Ancestors

Stoimenova, Vessela (2005)

Serdica Mathematical Journal

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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...

Indirect inference for survival data.

Bruce W. Turnbull, Wenxin Jiang (2003)

SORT

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In this paper we describe the so-called indirect method of inference, originally developed from the econometric literature, and apply it to survival analyses of two data sets with repeated events. This method is often more convenient computationally than maximum likelihood estimation when handling such model complexities as random effects and measurement error, for example; and it can also serve as a basis for robust inference with less stringent assumptions on the data generating mechanism....

Model selection for estimating the non zero components of a Gaussian vector

Sylvie Huet (2006)

ESAIM: Probability and Statistics

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We propose a method based on a penalised likelihood criterion, for estimating the number on non-zero components of the mean of a Gaussian vector. Following the work of Birgé and Massart in Gaussian model selection, we choose the penalty function such that the resulting estimator minimises the Kullback risk.

Robust estimation and forecasting for beta-mixed hierarchical models of grouped binary data.

Maxim A. Pashkevich, Yurij S. Kharin (2004)

SORT

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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...