Speaker: Arun Kumar

Abstract: Classical study of linear regression required many assumptions like linearity, homoscedasticity and normality which can be unrealistic. I will start with some results on linear regression that do not require any model assumptions and further do not require any randomness assumptions on the data. I will then briefly discuss extension of this result to other M-estimation problems (including logistic and Poisson regression). Implications such as uniform-in-submodel result of these results follow. The problem of post-selection inference (PoSI) in linear regression will be discussed based on these results. Finally some results on computation for PoSI will be announced.