The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
The Roles of Conditioning in Inference
Open accessAbstract
This paper reviews the use of sufficient and ancillary statistics in constructing conditional distributions for inference about a parameter. Special emphasis is given to recent developments in accurate approximation of densities, distribution functions and likelihood functions, and to the role of conditioning in these approximations. Exact conditional or marginal inference is available for essentially two classes of models, exponential family models and transformation family models. The approximations are very useful for practical implementation of these exact results. The form of the approximations suggests methods for inference in more general families.
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Citations
Cites
EFRON, B., & HINKLEY, D. V. (1978) · Biometrika · doi:10.1093/biomet/65.3.457 · Cited by 952
Berger, J. O., & Wolpert, R. L. (1988) · doi:10.1214/lnms/1215466210 · Cited by 616
Some Problems Connected with Statistical Inference
Cox, D. R. (1958) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177706618 · Cited by 536
Cited by
Exact inference for categorical data: recent advances and continuing controversies
Agresti, A. (2001) · Statistics in Medicine · doi:10.1002/sim.738 · Cited by 162
Metadata
- Authors
- Reid, N.
- Venue
- Statistical Science
- Publisher
- Institute of Mathematical Statistics
- Published
- 1995-05-01
- Volume
- 10
- Issue
- 2
- Pages
- 138-157
- ISSN
- 0883-4237 (ISSN-L)
- OpenAlex
- W2064783429
- Cited by
- 173
- References
- 3
- FWCI
- 6.51
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Reid, N. (1995). The Roles of Conditioning in Inference. Statistical Science. https://doi.org/10.1214/ss/1177010027
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
