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• W1909498639 abstract "Publisher Summary This chapter examines the problems of identification and inference for two error models prominent in robust estimation. The “contaminated sampling” model assumes that data errors are statistically independent of sample realizations from the population of interest. The more general “corrupted sampling” model does not impose this assumption. The contaminated sampling model is maintained in the part of the robustness literature concerned with bounded-influence estimation. The corrupted sampling model is used in studies of high-breakdown estimation. The chapter summarizes the findings of Horowitz and Manski 1995. It presents the contaminated and corrupted sampling models formally and provides conditions under which it is possible to obtain identified bounds on unidentified population parameters. It explains the way in which the bounds on certain parameters can be estimated, obtains asymptotic confidence regions for the bounds, and shows the way to test hypotheses about unidentified population parameters." @default.
• W1909498639 created "2016-06-24" @default.
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• W1909498639 date "1997-01-01" @default.
• W1909498639 modified "2023-09-27" @default.
• W1909498639 title "16 What can be learned about population parameters when the date are contaminated" @default.
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• W1909498639 doi "https://doi.org/10.1016/s0169-7161(97)15018-0" @default.
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