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Many of our seminars and public events this year will continue as in person or as hybrid (online and in person) events.

Please check our website listings and Twitter feed @STICERD_LSE for updates.

Unless otherwise specified, in-person seminars are open to the public. Please ensure you have informed the event contact as early as possible.

Those unable to join the seminars in-person are welcome to participate via zoom if the event is hybrid.

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About this event

We study the linear regression model with a scalar response and a functional predictor, a canonical example of an ill-posed inverse problem. We show that the functional partial least-squares (PLS) estimator achieves convergence rates that are nearly minimax-optimal over a class of ellipsoids and propose an adaptive early-stopping procedure for selecting the number of PLS components. In addition, we develop a new test that detects parametric local alternatives. The test can be inverted to construct confidence sets for the functional slope parameter. Simulation results show that the estimator performs favorably relative to several existing methods, and that the proposed test has good power. We apply our methodology to evaluate the nonlinear effects of temperature on corn and soybean yields.