The DOI matches its Crossref record; 63 of the 63 references it cites were found in a registry or an index.
Robust Statistical Modeling Using the t Distribution
Abstract
The t distribution provides a useful extension of the normal for statistical modeling of data sets involving errors with longer-than-normal tails. An analytical strategy based on maximum likelihood for a general model with multivariate t errors is suggested and applied to a variety of problems, including linear and nonlinear regression, robust estimation of the mean and covariance matrix with missing data, unbalanced multivariate repeated-measures data, multivariate modeling of pedigree data, and multivariate nonlinear regression. The degrees of freedom parameter of the t distribution provides a convenient dimension for achieving robust statistical inference, with moderate increases in computational complexity for many models. Estimation of precision from asymptotic theory and the bootstrap is discussed, and graphical methods for checking the appropriateness of the t distribution are presented.
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Cites
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Self-citationAn Analysis of Transformations
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Generalized additive models for location, scale and shape.
Rigby, R. A., & Stasinopoulos, D. M. (2005) · Journal of the Royal Statistical Society Series C: Applied Statistics · doi:10.1111/j.1467-9876.2005.00510.x · Cited by 3,228
Amelia II: A Program for Missing Data
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Bayesian estimation supersedes the t-test
Kruschke, J. K. (2013) · Journal of Experimental Psychology: General · doi:10.1037/a0029146 · Cited by 1,530
Modeling the Drop-Out Mechanism in Repeated-Measures Studies
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Metadata
- Authors
- Lange, K. L., Little, R. J. A., & Taylor, J. M. G.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 1989-12
- Volume
- 84
- Issue
- 408
- Pages
- 881-896
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W2118522007
- Cited by
- 1,039
- References
- 63
- FWCI
- 5.05
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
