31 citations · 60 across the 22 of their papers we have counts for
5 papers · 1 filter
Improving upon the effective sample size based on Godambe information for block likelihood inference
Rahul Mukerjee
We consider the effective sample size, based on Godambe information, for block likelihood inference which is an attractive and computationally feasible alternative to full likeliho…
Causal Inference from Possibly Unbalanced Split-Plot Designs: A Randomization-based Perspective
Rahul Mukerjee, Tirthankar Dasgupta
Split-plot designs find wide applicability in multifactor experiments with randomization restrictions. Practical considerations often warrant the use of unbalanced designs. This pa…
Causal Inference in Rebuilding and Extending the Recondite Bridge between Finite Population Sampling and Experimental Design
Rahul Mukerjee, Tirthankar Dasgupta, Donald B. Rubin
This article considers causal inference for treatment contrasts from a randomized experiment using potential outcomes in a finite population setting. Adopting a Neymanian repeated…
Highly Efficient Factorial Designs for cDNA Microarray Experiments: Use of Approximate Theory Together with a Step-up Step-down Procedure
Runchu Zhang, Rahul Mukerjee
A general method for obtaining highly efficient factorial designs of relatively small sizes is developed for cDNA microarray experiments. The method allows the main effects and int…
A Conversation with Shoutir Kishore Chatterjee
Tathagata Banerjee, Rahul Mukerjee
Shoutir Kishore Chatterjee was born in Ranchi, a small hill station in India, on November 6, 1934. He received his B.Sc. in statistics from the Presidency College, Calcutta, in 195…