Search arXivSearch

arXiv · 0906.4165

A Conversation with Pranab Kumar Sen

Abstract

Pranab Kumar Sen was born on November 7, 1937 in Calcutta, India. His father died when Pranab was 10 years old, so his mother raised the family of seven children. Given his superior performance on an exam, Pranab nearly went into medical school, but did not because he was underage. He received a B.Sc. degree in 1955 and an M.Sc. degree in 1957 in statistics from Calcutta University, topping the class both times. Dr. Sen's dissertation on order statistics and nonparametrics, under the direction of Professor Hari Kinkar Nandi, was completed in 1961. After teaching for three years at Calcutta University, 1961--1964, Professor Sen came to Berkeley as a Visiting Assistant Professor in 1964. In 1965, he joined the Departments of Statistics and Biostatistics at the University of North Carolina at Chapel Hill, where he has remained. Professor Sen's pioneering contributions have touched nearly every area of statistics. He is the first person who, in joint collaboration with Professor S. K. Chatterjee, developed multivariate rank tests as well as time-sequential nonparametric methods. He is also the first person who carried out in-depth research in sequential nonparametrics culminating in his now famous Wiley book Sequential Nonparametrics: Invariance Principles and Statistical Inference and SIAM monograph. Professor Sen has over 600 research publications. In addition, he has authored or co-authored 11 books and monographs, and has edited or co-edited 11 more volumes. He has supervised over 80 Ph.D. students, many of whom have achieved distinction both nationally and internationally. Professor Sen is the founding co-editor of two international journals: Sequential Analysis and Statistics and Decisions.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Malay Ghosh, Michael J. Schell. 2009-06-23. A Conversation with Pranab Kumar Sen. https://doi.org/10.1214/08-sts255

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Switchback Experiments under Geometric Mixing

The switchback is an experimental design that measures treatment effects by repeatedly turning an intervention on and off for a whole system. Switchback experiments are a robust way to overcome cross-unit spillover effects; however, they are vulnerable to bias from temporal carryovers. In this paper, we consider properties of switchback experiments in Markovian systems that mix at a geometric rate. We find that, in this setting, standard switchback designs suffer considerably from carryover bias: Their estimation error decays as $T^{-1/3}$ in terms of the experiment horizon $T$, whereas in the absence of carryovers a faster rate of $T^{-1/2}$ would have been possible. We also show, however, that judicious use of burn-in periods can considerably improve the situation, and enables errors that decay as $\log(T)^{1/2}T^{-1/2}$. Our formal results are mirrored in an empirical evaluation.

stat.ME

Saddlepoint approximations for plug-in resampling

Resampling-based procedures can improve on normal approximations in sparse, large-scale testing problems, but their computational cost can be prohibitive. We recognize that several existing procedures belong to a faster plug-in resampling subclass, fixing fitted nuisance parameters during resampling. When the resampled statistic is a sum of conditionally independent terms, the saddlepoint approximation (SPA) for the resampling $p$-value offers further acceleration, replacing resampling with an analytical tail approximation. However, standard Edgeworth-based approximation-error bounds impose regularity conditions that are hard to verify for plug-in resampling laws. We use an alternative approach to establish a finite-sample relative-error bound for the Lugannani-Rice approximation under more tractable conditions, which we apply in two contexts. In statistical genetics, we identify response resampling procedures as the targets of existing SPAs and establish guarantees in a representative setting. In conditional independence testing, we introduce spaCRT, an SPA for the distilled conditional randomization test (dCRT), which has been applied successfully in biology. Our rates quantify the effects of sparsity and signal strength, with matching lower bounds in special cases. We additionally establish asymptotic Type-I error control of the corresponding plug-in resampling procedures under growing sparsity. In simulations and single-cell CRISPR data analysis, spaCRT closely approximates dCRT $p$-values and preserves its statistical performance while accelerating computation by up to 250-fold.

stat.ME

NIRVAR: Network Informed Restricted Vector Autoregression

High-dimensional panels of time series often arise in finance and macroeconomics, where co-movements within groups of panel components occur. Extracting these groupings from the data provides a coarse-grained description of the complex system in question and can inform subsequent prediction tasks. We develop a novel methodology to model such a panel as a restricted vector autoregressive process, where the coefficient matrix is the weighted adjacency matrix of a stochastic block model. This network time series model, which we call the Network Informed Restricted Vector Autoregression (NIRVAR) model, yields a coefficient matrix that has a sparse block-diagonal structure. We propose an estimation procedure that embeds each panel component in a low-dimensional latent space and clusters the embedded points to recover the blocks of the coefficient matrix. Crucially, the method allows for network-based time series modelling when the underlying network is unobserved. We derive the bias, consistency and asymptotic normality of the NIRVAR estimator. Simulation studies suggest that the NIRVAR estimated embedded points are Gaussian distributed around the ground truth latent positions. On three applications to finance, macroeconomics, and transportation systems, NIRVAR outperforms competing models in terms of prediction and provides interpretable results regarding group recovery.

stat.ME