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Robust Estimation and Prediction in the Presence of Influential Units in SurveysTeng, Yizhen 02 August 2023 (has links)
In surveys, one may face the problem of influential units at the estimation stage.
A unit is said to be influential if its inclusion or exclusion from the sample has a
drastic impact on the estimates. This is a common situation in business surveys
as the distribution of economic variables tends to be highly skewed. We study and
examine some commonly used estimators and predictors of a population total and
propose a robust estimator and predictor based on an adaptive tuning constant. The
proposed tuning constant is based on the concept of conditional bias of a unit, which
is a measure of influence. We present the results of a simulation study that compares
the performance of several estimators and predictors in terms of bias and efficiency.
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Estimation multi-robuste efficace en présence de données influentesMichal, Victoire 08 1900 (has links)
No description available.
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Finite population inference for population with a large number of zero-valued observationsNolet-Pigeon, Isabelle 08 1900 (has links)
Dans certaines enquêtes auprès des entreprises, il n'est pas rare de s'intéresser à estimer le total ou la moyenne d'une variable qui, par sa nature, prend souvent une valeur nulle. En présence d'une grande proportion de valeurs nulles, les estimateurs usuels peuvent s'avérer inefficaces. Dans ce mémoire, nous étudions les propriétés des estimateurs habituels pour des populations exhibant une grande proportion de zéros. Dans un contexte d'une approche fondée sur le modèle, nous présentons des prédicteurs robustes à la présence de valeurs influentes pour ce type de populations. Finalement, nous effectuons des études par simulation afin d'évaluer la performance de divers estimateurs/prédicteurs en termes de biais et d'efficacité. / In business surveys, we are often interested in estimating population means or totals of variables which, by nature, will often take a value of zero. In the presence of a large proportion of zero-valued observations, the customary estimators may be unstable. In this thesis, we study the properties of commonly used estimators for populations exhibiting a large proportion of zero-valued observations. In a model-based framework, we present some robust predictors in the presence of influential units. Finally, we perform simulation studies to evaluate the performance of several estimators in terms of bias and efficiency.
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