fall_2023
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Time and place:
–,
Erling Sverdrups plass, Niels Henrik Abels hus, 8th floor
This paper considers hypothesis testing in semiparametric models which may be non – regular for certain values of a (potentially infinite dimensional) nuisance parameter. In such models no (locally) regular estimator of the parameter of interest exists. The situation for testing is somewhat different: I establish that C(α) – style test statistics achieve their limiting distributions in a (locally) regular manner under mild conditions, leading to tests with correct size in situations where standard tests fail to control size. Additionally, I characterise the appropriate limit experiment in which to study local (asymptotic) optimality of tests in the case where the efficient information matrix is singular. This permits the generalisation of classical power bounds to the non – regular case. I provide appropriate statements of these bounds and give conditions under which they are attained by the proposed C(α) – style tests. Three examples are worked out in detail.
Time and place:
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Erling Svedrups plass and Zoom https://uio.zoom.us/j/64912028556?pwd=QmJpa1ZPS0hBNTFZUDhzWDlaMmJKQT09
Traditional quantile estimators are not well-suited for data streams because the memory and computational time increase with the volume of data received from the stream. Incremental quantile estimators refer to a class of methods designed to maintain quantile estimates for data streams. These methods operate by making small updates to the estimate every time a new observation is received from the stream. In this presentation, I will introduce some of the incremental quantile estimators we have developed.
Time and place:
–,
Erling Svedrups plass and Zoom https://uio.zoom.us/j/66849037258?pwd=NkNiR0lkbm5VK0VyMytVZW4vV0hNQT09