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An Empirical Investigation of Goodness-of-Fit Statistics for Sparse Multinomials
Open accessAbstract
Traditional discussions of goodness-of-fit tests for multinomial data consider asymptotic chi-squared properties under the assumption that all expected cell frequencies become large. This condition is not always satisfied, however, and another asymptotic theory must be considered. For testing a specified simple hypothesis, Morris (1975) and Hoist (1972) gave conditions for the asymptotic normality of the Pearson and likelihood ratio statistics when both the sample size and number of cells become large (even if the expected cell frequencies remain small). Monte Carlo techniques are used to examine the applicability of the normal approximations for moderate sample sizes with moderate numbers of cells.
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Some Methods for Strengthening the Common χ 2 Tests
Cochran, W. G. (1954) · Biometrics · doi:10.2307/3001616 · Cited by 3,718
Cited by
Log-Linear Models for Capture-Recapture
Cormack, R. M. (1989) · Biometrics · doi:10.2307/2531485 · Cited by 313
Working memory failure in phone-based interaction
Huguenard, B. R., Lerch, F. J., Junker, B. W., Patz, R. J., & Kass, R. E. (1997) · ACM Transactions on Computer-Human Interaction · doi:10.1145/254945.254947 · Cited by 34
Metadata
- Authors
- Koehler, K. J., & Larntz, K.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 1980-06
- Volume
- 75
- Issue
- 370
- Pages
- 336-344
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W2155491684
- Cited by
- 289
- References
- 1
- FWCI
- 4.14
- Type
- journal article
- Language
- en
- Open access
- green
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- Koehler, K. J., & Larntz, K. (1980). An Empirical Investigation of Goodness-of-Fit Statistics for Sparse Multinomials. Journal of the American Statistical Association. https://doi.org/10.1080/01621459.1980.10477473
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