Seminar Series: Snigdha Panigrahi

My talk today will be based on addressing problems of overcoming selection-bias in a modern, iterative framework of science-- where, the researcher begins with an initial data set which she might use for selection. But, usually further observations are made available at a future point in time either because the researcher decided to collect more data after seeing the outcome of the initial analysis, or simply because another data set comes in later on. At this point, the researcher is confronted with the question of: how to combine the two data sets to provide inference for parameters selected based only on the first data set.

 

A compellingly simple way is to consider inference based on a split-likelihood, using only on the second data set which has not been used for selection. I will take a more optimal and less wasteful approach (than splitting) to this problem, dubbed as "carving" and cast the described two-staged scientific procedures into a conditional framework. Introducing a "carved-likelihood", I will talk about a mathematical framework to conduct model-free inference based upon the same and discuss an implementation toolbox for practitioners.