Abstract
This book grew out of a desire to bridge the gap between parametric and nonparametric statistics and to exploit the best aspects of the former while enjoying the robustness properties of the latter. Parametric statistics is a well-established field which incorporates the important notions of likelihood and sufficiency that are part of estimation and testing. Likelihood methods have been used to construct efficient estimators, confidence intervals, and tests with good power properties.
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References
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Alvo, M., Yu, P.L.H. (2018). Introduction. In: A Parametric Approach to Nonparametric Statistics. Springer Series in the Data Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-94153-0_1
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DOI: https://doi.org/10.1007/978-3-319-94153-0_1
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Publisher Name: Springer, Cham
Print ISBN: 978-3-319-94152-3
Online ISBN: 978-3-319-94153-0
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