Publication

Méthodes de rééchantillonnage pour l'estimation de variance

Anthony Christopher Davison, Sylvain Sardy
2006
Journal Articles

Abstract

Nous passons en revue des techniques de rééchantillonnage utilisées pour l'estimation de variance en sondage. Les techniques de rééchantillonnage considérées sont basées sur la linéarisation, le jackknife, les répétitions équilibrées répétées, et le bootstrap. Quelques unes des procédures d'estimation de variance utilisent des mécanismes de calibration et d'imputation afin d'améliorer l'estimation. Notre objectif est d'obtenir des conclusions pratiques basées sur des considérations théoriques et des comparaisons empiriques.

Official source

https://infoscience.epfl.ch/entities/publication/aa1e77d6-2e72-4c63-b95a-8ecca92d0449

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Anthony Christopher Davison, Sylvain Sardy
2006
Journal Articles

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/aa1e77d6-2e72-4c63-b95a-8ecca92d0449

About this result

Related concepts (10)

Least squares

The method of least squares is a standard approach in regression analysis to approximate the solution of overdetermined systems (sets of equations in which there are more equations than unknowns) by minimizing the sum of the squares of the residuals (a residual being the difference between an observed value and the fitted value provided by a model) made in the results of each individual equation. The most important application is in data fitting.

Estimation

Estimation (or estimating) is the process of finding an estimate or approximation, which is a value that is usable for some purpose even if input data may be incomplete, uncertain, or unstable. The value is nonetheless usable because it is derived from the best information available. Typically, estimation involves "using the value of a statistic derived from a sample to estimate the value of a corresponding population parameter".

Estimation theory

Estimation theory is a branch of statistics that deals with estimating the values of parameters based on measured empirical data that has a random component. The parameters describe an underlying physical setting in such a way that their value affects the distribution of the measured data. An estimator attempts to approximate the unknown parameters using the measurements.

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