Abstract
Abstract This article describes a generalized program for the computation of sampling errors. It employs computerized linearization of nonlinear estimates by the use of the first-order Taylor approximation. It can be used for any estimate derived from any "large" probability sample. In most instances the only inputs required are the weighted sample data and the form of the estimate whose precision is to be measured. In these cases, both the estimate and its sampling error can be produced with the same amount of data preparation and programming effort as is required to produce the estimate only.
Keywords
Affiliated Institutions
Related Publications
Balanced Repeated Replications for Standard Errors
Abstract Balanced repeated replications (BRR) is a general method for computing standard errors. It is useful when mathematical distribution theory is impractical or lacking, an...
The Jackknife, the Bootstrap and Other Resampling Plans
The Jackknife Estimate of Bias The Jackknife Estimate of Variance Bias of the Jackknife Variance Estimate The Bootstrap The Infinitesimal Jackknife The Delta Method and the Infl...
Bootstrap Methods: Another Look at the Jackknife
We discuss the following problem: given a random sample $\\mathbf{X} = (X_1, X_2, \\cdots, X_n)$ from an unknown probability distribution $F$, estimate the sampling distribution...
Estimating Mean and Standard Deviation from the Sample Size, Three Quartiles, Minimum, and Maximum
Background: We sometimes want to include in a meta-analysis data from studies where results are presented as medians and ranges or interquartile ranges rather than as means and ...
CODA: convergence diagnosis and output analysis for MCMC
[1st paragraph] At first sight, Bayesian inference with Markov Chain Monte Carlo (MCMC) appears to be straightforward. The user defines a full probability model, perhaps using o...
Publication Info
- Year
- 1976
- Type
- article
- Volume
- 71
- Issue
- 354
- Pages
- 315-321
- Citations
- 66
- Access
- Closed
External Links
Social Impact
Social media, news, blog, policy document mentions
Citation Metrics
Cite This
Identifiers
- DOI
- 10.1080/01621459.1976.10480338