Abstract
In this chapter we study conditional likelihood-based inference in discrete outcome problems. This method is very useful for sparse data where there exists a large number of nuisance parameters. Moreover it is used extensively in matched case-control studies where some baseline covariates or survival times are matched at the data collection stage.
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© 2017 Springer Nature Singapore Pte Ltd.
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Qin, J. (2017). Conditioning Approach for Discrete Outcome Problems. In: Biased Sampling, Over-identified Parameter Problems and Beyond. ICSA Book Series in Statistics. Springer, Singapore. https://doi.org/10.1007/978-981-10-4856-2_12
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DOI: https://doi.org/10.1007/978-981-10-4856-2_12
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Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-4854-8
Online ISBN: 978-981-10-4856-2
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