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inteli (version 0.1.2)

inteli-package: Interval Estimation by Likelihood Method

Description

Parameter estimation via likelihood interval (LI) compared to conventional method (CI).

Arguments

Author

Maintainer: Minkyu Kim mkim@acr.kr

Authors:

Details

Currently used CI method has its limitation when the test statistics are asymmetrical (chi-square test, F-test) or the model functions are non-linear. It can be overcome by using the likelihood functions for the interval estimation. 'inteli' package now supports interval estimation for the mean, variance, variance ratio, binomial distribution, Poisson distribution, odds ratio, risk difference, relative risk and their likelihood function plots. Testing functions are also provided.

References

  1. Wilks SS. The Large-sample Distribution of the Likelihood Ratio for Testing Composite Hypotheses. Ann Math Stat. 1938;9(1):60-62.

  2. Edwards AWF. Likelihood. 1972.

  3. Fisher RA. Statistical Methods and Scientific Inference. 3e. 1973.

  4. Bates DM, Watts DG. Nonlinear Regression Analysis and its Application. 1988.

  5. Ruppert D, Cressie N, Carroll RJ. A Transformation/Weighting Model for Estimating Michaelis-Menten Parameters. Cornell University Technical Report 796. 1988.

  6. Royall R. Statistical Evidence. 1997.

  7. Pinheiro JC, Bates DM. Mixed Effects Models in S and S-PLUS. 2000.

  8. Pawitan Y. In All Likelihood: Statistical Modelling and Inference Using Likelihood. 2001.

  9. Lehmann EL. Fisher, Nayman, and the Creation of Classical Statistics. 2011.

  10. Rohde CA. Introductory Statistical Inference with the Likelihood Function. 2014.

  11. Held L, Bove DS. Likelihood and Bayesian Inference. 2020.

  12. Lee MH, Bae KS. Likelihood interval for nonlinear regression. 2023.