Dominique Fourdrinier is a Professor of Mathematical Statistics at the University of Rouen in France and an Adjunct Professor of Statistical Science at Cornell University. He earned his M.S. and Ph.D. degrees, both in Mathematical Statistics, at the University of Rouen. He is noted for his deep insights on the connections between shrinkage estimation and the properties of differential operators and has made important contributions to Bayesian statistics, decision theory, estimation theory, spherical and elliptical symmetry, the Stein phenomena as well as to statistical methods for signal and image processing. William E. Strawderman is a Professor of Statistics at Rutgers University. He earned an M.S. in Mathematics from Cornell University and a second M.S. in Statistics from Rutgers, and then completed his Ph.D. in Statistics, also at Rutgers. He is a fellow of both the Institute of Mathematical Statistics and American Statistical Society and an Elected Member, International Statistical Institute. In 2015 he was named a Distinguished Alumni at Cornell. He is noted for path-breaking work in shrinkage estimation and has made fundamental contributions to a number of additional areas in statistics, including Bayesian statistics, decision theory, spherical symmetry, and biostatistics.Martin T. Wells is the Charles A. Alexander Professor of Statistical Sciences at Cornell University. He is also a Professor of Social Statistics, Professor of Biostatistics and Epidemiology at Weill Cornell Medicine as well as an Elected Member of the Cornell Law School Faculty. He is a fellow of both the Institute of Mathematical Statistics and American Statistical Society and an Elected Member, International Statistical Institute. His research interests include Bayesian statistics, biostatistics, decision theory, empirical legal studies, machine learning, and statistical genomics.

• Chapter 1. Decision Theory Preliminaries.- Chapter 2. Estimation of a normal mean vector I.- Chapter 3. Estimation of a normal mean vector II.- Chapter 4. Spherically symmetric distributions.- Chapter 5. Estimation of a mean vector for spherically symmetric distributions I: known scale.- Chapter 6. Estimation of a mean vector for spherically symmetric distributions II: with a residual.- Chapter 7. Restricted Parameter Spaces.- Chapter 8. Loss and Confidence Level Estimation.-

“This book a timely and well-written exposition of shrinkage, or Stein, estimation intended for graduate students and researchers who wish to learn more about the topic.” (Éric Marchand, Mathematical Reviews, August, 2019)“The well-written volume, presenting the actual knowledge in this field, is suitable for readers having good background in analysis, linear algebra, probability theory and mathematical statistics.” (Kurt Marti, zbMATH 1411.62011, 2019)

Dominique Fourdrinier is a Professor of Mathematical Statistics at the University of Rouen in France and an Adjunct Professor of Statistical Science at Cornell University. He earned his M.S. and Ph.D. degrees, both in Mathematical Statistics, at the University of Rouen. He is noted for his deep insights on the connections between shrinkage estimation and the properties of differential operators and has made important contributions to Bayesian statistics, decision theory, estimation theory, spherical and elliptical symmetry, the Stein phenomena as well as to statistical methods for signal and image processing. William E. Strawderman is a Professor of Statistics at Rutgers University. He earned an M.S. in Mathematics from Cornell University and a second M.S. in Statistics from Rutgers, and then completed his Ph.D. in Statistics, also at Rutgers. He is a fellow of both the Institute of Mathematical Statistics and American Statistical Society and an Elected Member, International Statistical Institute. In 2015 he was named a Distinguished Alumni at Cornell. He is noted for path-breaking work in shrinkage estimation and has made fundamental contributions to a number of additional areas in statistics, including Bayesian statistics, decision theory, spherical symmetry, and biostatistics.Martin T. Wells is the Charles A. Alexander Professor of Statistical Sciences at Cornell University. He is also a Professor of Social Statistics, Professor of Biostatistics and Epidemiology at Weill Cornell Medicine as well as an Elected Member of the Cornell Law School Faculty. He is a fellow of both the Institute of Mathematical Statistics and American Statistical Society and an Elected Member, International Statistical Institute. His research interests include Bayesian statistics, biostatistics, decision theory, empirical legal studies, machine learning, and statistical genomics.

• Chapter 1. Decision Theory Preliminaries.- Chapter 2. Estimation of a normal mean vector I.- Chapter 3. Estimation of a normal mean vector II.- Chapter 4. Spherically symmetric distributions.- Chapter 5. Estimation of a mean vector for spherically symmetric distributions I: known scale.- Chapter 6. Estimation of a mean vector for spherically symmetric distributions II: with a residual.- Chapter 7. Restricted Parameter Spaces.- Chapter 8. Loss and Confidence Level Estimation.-

“This book a timely and well-written exposition of shrinkage, or Stein, estimation intended for graduate students and researchers who wish to learn more about the topic.” (Éric Marchand, Mathematical Reviews, August, 2019)“The well-written volume, presenting the actual knowledge in this field, is suitable for readers having good background in analysis, linear algebra, probability theory and mathematical statistics.” (Kurt Marti, zbMATH 1411.62011, 2019)

Dominique Fourdrinier is a Professor of Mathematical Statistics at the University of Rouen in France and an Adjunct Professor of Statistical Science at Cornell University. He earned his M.S. and Ph.D. degrees, both in Mathematical Statistics, at the University of Rouen. He is noted for his deep insights on the connections between shrinkage estimation and the properties of differential operators and has made important contributions to Bayesian statistics, decision theory, estimation theory, spherical and elliptical symmetry, the Stein phenomena as well as to statistical methods for signal and image processing. William E. Strawderman is a Professor of Statistics at Rutgers University. He earned an M.S. in Mathematics from Cornell University and a second M.S. in Statistics from Rutgers, and then completed his Ph.D. in Statistics, also at Rutgers. He is a fellow of both the Institute of Mathematical Statistics and American Statistical Society and an Elected Member, International Statistical Institute. In 2015 he was named a Distinguished Alumni at Cornell. He is noted for path-breaking work in shrinkage estimation and has made fundamental contributions to a number of additional areas in statistics, including Bayesian statistics, decision theory, spherical symmetry, and biostatistics.Martin T. Wells is the Charles A. Alexander Professor of Statistical Sciences at Cornell University. He is also a Professor of Social Statistics, Professor of Biostatistics and Epidemiology at Weill Cornell Medicine as well as an Elected Member of the Cornell Law School Faculty. He is a fellow of both the Institute of Mathematical Statistics and American Statistical Society and an Elected Member, International Statistical Institute. His research interests include Bayesian statistics, biostatistics, decision theory, empirical legal studies, machine learning, and statistical genomics.

• Chapter 1. Decision Theory Preliminaries.- Chapter 2. Estimation of a normal mean vector I.- Chapter 3. Estimation of a normal mean vector II.- Chapter 4. Spherically symmetric distributions.- Chapter 5. Estimation of a mean vector for spherically symmetric distributions I: known scale.- Chapter 6. Estimation of a mean vector for spherically symmetric distributions II: with a residual.- Chapter 7. Restricted Parameter Spaces.- Chapter 8. Loss and Confidence Level Estimation.-

“This book a timely and well-written exposition of shrinkage, or Stein, estimation intended for graduate students and researchers who wish to learn more about the topic.” (Éric Marchand, Mathematical Reviews, August, 2019)“The well-written volume, presenting the actual knowledge in this field, is suitable for readers having good background in analysis, linear algebra, probability theory and mathematical statistics.” (Kurt Marti, zbMATH 1411.62011, 2019)