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Title: Estimation and Inference on Semiparametric Regression Models


For regression models, it is challenging to obtain point and variance estimates of regression parameters if the corresponding estimating functions are nonregular, such as non-smooth and non-monotone. We discuss the issues and present recently developed new approaches based on a natural self-induced smoothing method. We show general theory, implementation, simulation studies and demonstrate the methods with censored linear regression models and general semiparametric transformation models.