This volume highlights Prof. Hira Koul’s achievements in many areas of Statistics, including Asymptotic theory of statistical inference, Robustness, Weighted empirical processes and their applications, Survival Analysis, Nonlinear time series and Econometrics, among others. Chapters are all original papers that explore the frontiers of these areas and will assist researchers and graduate students working in Statistics, Econometrics and related areas. Prof. Hira Koul was the first Ph.D. student of Prof. Peter Bickel. His distinguished career in Statistics includes the receipt of many prestigious awards, including the Senior Humbolt award (1995), and dedicated service to the profession through editorial work for journals and through leadership roles in professional societies, notably as the past president of the International Indian Statistical Association. Prof. Hira Koul has graduated close to 30 Ph.D. students, and made several seminal contributions in about 125 innovative research papers. The long list of his distinguished collaborators is represented by the contributors to this volume.
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1 Professor Hira Lal Koul's Contribution to Statistics |
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1 | (8) |
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2 Martingale Estimating Functions for Stochastic Processes: A Review Toward a Unifying Tool |
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9 | (20) |
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3 Asymptotics of Lλ-Norms of ARCH(p) Innovation Density Estimators |
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29 | (12) |
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4 Asymptotic Risk and Bayes Risk of Thresholding and Superefficient Estimates and Optimal Thresholding |
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41 | (28) |
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5 A Note on Nonparametric Estimation of a Bivariate Survival Function Under Right Censoring |
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69 | (16) |
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6 On Equality in Distribution of Ratios X/(X + Y) and Y/(X + Y) |
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85 | (6) |
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7 Nonparametric Distribution-Free Model Checks for Multivariate Dynamic Regressions |
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91 | (28) |
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8 Ridge Autoregression R-Estimation: Subspace Restriction |
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119 | (18) |
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9 On Hodges and Lehmann's "6/Φ Result" |
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137 | (18) |
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10 Fiducial Theory for Free-Knot Splines |
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155 | (36) |
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11 An Empirical Characteristic Function Approach to Selecting a Transformation to Symmetry |
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191 | (12) |
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12 Averaged Regression Quantiles |
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203 | (14) |
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13 A Study of One Null Array of Random Variables |
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217 | (10) |
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14 Frailty, Profile Likelihood, and Medfly Mortality |
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227 | (12) |
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15 Comparison of Autoregressive Curves Through Partial Sums of Quasi-Residuals |
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239 | (18) |
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16 Testing for Long Memory Using Penalized Splines and Adaptive Neyman Methods |
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257 | (22) |
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17 On The Computation of R-Estimators |
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279 | (10) |
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18 Multiple Change-Point Detection in Piecewise Exponential Hazard Regression Models with Long-Term Survivors and Right Censoring |
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289 | (16) |
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19 How to Choose the Number of Gradient Directions for Estimation Problems from Noisy Diffusion Tensor Data |
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305 | (6) |
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20 Efficient Estimation in Two-Sided Truncated Location Models |
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311 | (18) |
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21 Semiparametric Analysis of Treatment Effect via Failure Probability Ratio and the Ratio of Cumulative Hazards |
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329 | (24) |
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22 Inference for the Standardized Median |
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353 | (12) |
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23 Efficient Quantile Regression with Auxiliary Information |
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365 | (10) |
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24 Nonuniform Approximations for Sums of Discrete m-Dependent Random Variables |
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375 | (20) |
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| About the Editors |
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Soumendra Lahiri is a Professor of Statistics at the North Carolina State University. His research interests include Nonparametric Statistics, Time Series, Spatial Statistics, and Statistical inference for high dimensional data. He served as an Editor of Sankhya, Series A (2007-2009) and currently, he is on the editorial boards of the Annals of Statistics and the Journal of Statistical Planning and Inference. He is a fellow of the ASA and the IMS, and an elected member of the International Statistical Institute.
Anton Schick is Professor and Chair of the Department of Mathematical Sciences at Binghamton University. His research interests include semiparametric efficiency, U-statistics, empirical likelihood, residual-based inference, incomplete data, curve estimation, and inference for stochastic processes. He currently serves on the editorial boards of Statistics and Probability Letters, Statistical Inference for Stochastic Processes, and the Journal of the Indian Statistical Association.
Ashis SenGupta is Professor (HAG), at Indian Statistical Institute, Kolkata. His research interests are in Multivariate Analysis and Inference, Probability distributions on manifolds, Directional Statistics, Reliability and Environmental Statistics. He has been a frequent vistor to several universities in USA as a visiting faculty including Stanford University; University of Wisconsin - Madison; University of California - Santa Barabara and Riverside; and Michigan State University-East Lansing. He is an Editor-in-Chief of Environmental & Ecological Statistics, Springer, USA, and an Editor of Scientiae Mathematicae Japonicae, Japan. He has been President of International Indian Statistical Association (India Chapter) for several successive terms. He is a Member of the Project Advisory Committee - Math. Sciences, DST, Government of India; a Fellow of National Academy of Sciences, India and a Fellow of American StatisticalAssociation, USA
T. N. Sriram is currently a Professor at the Department of Statistics, University of Georgia (UGA). He is a Fellow of the American Statistical Association and the winner of Special Sandy Beaver Teaching Award at UGA. His research interests focus on theory and applications for a variety of statistical scenarios, such as sequential analysis for independent & dependent data; bootstrap methods for linear & non-linear time series, and branching processes; robust estimation methods for mixture models; dimension reduction methods and its robust modifications in time series and in association studies; and sample size determination for classifiers arising in biological studies. His research has been funded by federal agencies such as National Science Foundation, National Security Agency, and the Bureau of Labor Statistics. He has directed eleven doctoral dissertations, served as an editorial board member of three statistics journals, organized three international conferences, and served on NSF panels.
