244 Pages
2 Color & 13 B/W Illustrations
by
Chapman & Hall
This book provides a systematic treatment of two central regimes in statistical theory: classical large-sample theory for M- and Z-estimation with a fixed number of parameters, and high-dimensional theory where the number of parameters can be comparable to or larger than the sample size. While the former was developed earlier and remains fundamental, high-dimensional statistical theory has become... Read more
Preface Author Biography 1 Basic convergence theory 2 Classical theory for M- and Z-estimation 3 Concentration inequalities 4 High-dimensional linear regression 5 High-dimensional generalized linear regression 6 High-dimensional inference for regression coefficients Bibliography Index
Biography
Zhiqiang Tan is a Distinguished Professor in the Department of Statistics at Rutgers University. His research and teaching interests include Monte Carlo methods, causal inference, statistical learning, and related areas. He is a Fellow of the American Statistical Association, a Fellow of the Institute of Mathematical Statistics, and an Elected Member of the International Statistical Institute.
