Weights are numbers attached to observations in order to get unbiased and/or more efficient parameter estimates. These are used in stratified sampling, in regression when error variances are not homogeneous, in robust estimation when the sample contains heavier tails than that of the normal distribution or outliers, and in meta-analysis. When weights are updated iteratively, complicated maximum likelihood estimators (MLEs) can be obtained through simple formulas. Well-known examples in this direction are the generalized linear models and models based on t distributions.

Weights in Stratified Sampling

Stratified sampling is a method of sampling within subpopulations. Each subpopulation is called a stratum. When strata vary considerably in size or distribution, stratified sampling often improves the representativeness of the sample. By assigning a proper weight to each stratum, ...

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