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Cambridge University Press. Cambridge University Press, This book is an introduction to the field of asymptotic statistics. The treatment is both practical and mathematically rigorous. Suitable as a graduate or Master's level statistics text, this book will also give researchers an overview of the latest research in asymptotic statistics.

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Be the first to write a review. Add to Wishlist. Ships in 10 to 15 business days. Link Either by signing into your account or linking your membership details before your order is placed. Description Table of Contents Product Details Click on the cover image above to read some pages of this book! Industry Reviews 'The book is extremely well written and clear Introduction Stochastic convergence The delta-method Moment estimators M- and Z-estimators Contiguity Local asymptotic normality Efficiency of estimators Limits of experiments Bayes procedures Projections U-statistics Rank, sign, and permutation statistics Relative efficiency of tests Efficiency of tests Likelihood ratio tests Chi-square tests Stochastic convergence in metric spaces Empirical processes The functional delta-method Quantiles and order statistics L-statistics The bootstrap Nonparametric density estimation Semiparametric models Table of Contents provided by Publisher.

How do series work? Helpers cpg 30 , Edward 3 , vitus 1 , AnnaClaire 1 , ifethereal 1.

Princeton University Library Catalog

Bootstrap Methods and Their Application by A. Markov Chains by J. Asymptotic Statistics by A. Bayesian Methods by Thomas Leonard. The Estimation and Tracking of Frequency by B.

S- Statistics and Their Basic Properties | SpringerLink

Statistical Models by A. Semiparametric Regression by David Ruppert.

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Exercises in Probability by Loic Chaumont. Essentials of Statistical Inference by G. Elements of Distribution Theory by Thomas A. Random Graph Dynamics by Rick Durrett.