Titlebook: Biased Sampling, Over-identified Parameter Problems and Beyond; Jing Qin Book 2017 Springer Nature Singapore Pte Ltd. 2017 Biased Sampling

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https://doi.org/10.1007/978-3-658-31493-4The projection method can be used not only in finitely many parameter problems but also in nuisance function or infinite many nuisance parameters cases.

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Schlussfolgerung und Diskussion,The maximum likelihood method for regular parametric models has many optimality properties. As a result, it is one of the most popular methods in statistical inference. However, model mis-specification is a big concern since a misspecified model may lead to bias results.

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https://doi.org/10.1007/978-3-642-11710-7Besides empirical likelihood, the Kullback–Leibler likelihood is another popular method to calibrate auxiliary information. The entropy family has also been used extensively in information theory. We mainly focus on discussions for continuous random variable cases. The discrete cases can be treated similarly.

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https://doi.org/10.1007/978-3-322-95257-8In 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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Internet - Bildung - GemeinschaftIn this Chapter we present the results by Qin and Zhang (Biometrika 92:251–270, 2005) and Li and Qin (JASA 496:1476–1484, 2011) on the connection between marginal likelihood, conditional likelihood and empirical likelihood.