Maximum likelihood estimator and likelihood ratio test in complex models: An application to B lymphocyte development
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In this paper we introduce a simple framework which provides a basis for estimating parameters and testing statistical hypotheses in complex models. The only assumption that is made in the model describing the process under study, is that the deviations of the observations from the model have a multivariate normal distribution. The application of the statistical techniques presented in this paper may have considerable utility in the analysis of a wide variety of complex biological and epidemiological models. To our knowledge, the model and methods described here have not previously been published in the area of theoretical immunology.
KeywordsMaximum Likelihood Estimator Label Cell Biological Model Likelihood Ratio Test Statistic Lymphocyte Development
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- Detours, V., R. Mehr and A. Perelson (2000). Deriving quantitative constrains under which T cell selection operates from data on the mature T cell repertoire. J. Immunol. 164, 121–128.Google Scholar
- Ferguson, S. F. (1996). A Course in Large Sample Theory, London, UK: Chapman and Hall.Google Scholar
- Kirschner, D. (2001). Reconstructing microbial pathogenesis. ASM News 67, 567–573.Google Scholar
- Kirschner, D., R. Mehr and A. Perelson (1998). The role of the thymus in HIV infection. J. AIDS Hum. Retrovirol. 18, 95–109.Google Scholar
- Lehmann, E. L. and G. Casella (1998). Theory of Point Estimation, 2nd edn, NY: Springer.Google Scholar
- Mehr, R. and A. Perelson (1997). Blind homeostasis and the CD4:CD8 ratio in the thymus and peripheral blood. J. AIDS Hum. Retrovirol. 14, 387–398.Google Scholar
- Shannon, M. and R. Mehr (1999). Reconciling repertoire shift with affinity maturation: the role of deleterious mutations. J. Immunol. 162, 3950–3956.Google Scholar
- Shlomchik, M., P. Watts, M. Weigert and S. Litwin (1998). Clone: a Monte-Carlo computer simulation of B cell clonal expansion, somatic mutation, and antigen-driven selection. Curr. Top. Microbiol. Immunol. 229, 173–197.Google Scholar