## Asymptotic StatisticsThis book is an introduction to the field of asymptotic statistics. The treatment is both practical and mathematically rigorous. In addition to most of the standard topics of an asymptotics course, including likelihood inference, M-estimation, the theory of asymptotic efficiency, U-statistics, and rank procedures, the book also presents recent research topics such as semiparametric models, the bootstrap, and empirical processes and their applications. The topics are organized from the central idea of approximation by limit experiments, which gives the book one of its unifying themes. This entails mainly the local approximation of the classical i.i.d. set up with smooth parameters by location experiments involving a single, normally distributed observation. Thus, even the standard subjects of asymptotic statistics are presented in a novel way. 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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### Contents

Stochastic Convergence | 7 |

Delta Method | 28 |

Moment Estimators | 37 |

Contiguity | 86 |

Local Asymptotic Normality | 93 |

Efficiency of Estimators | 108 |

Limits of Experiments | 125 |

Bayes Procedures | 138 |

Likelihood Ratio Tests 227 16 Likelihood Ratio Tests | 227 |

ChiSquare Tests | 242 |

Stochastic Convergence in Metric Spaces | 255 |

Empirical Processes | 265 |

Functional Delta Method | 291 |

Quantiles and Order Statistics | 304 |

Bootstrap | 326 |

Nonparametric Density Estimation | 341 |

Projections | 153 |

UStatistics | 161 |

Rank Sign and Permutation Statistics | 173 |

Relative Efficiency of Tests | 192 |

Efficiency of Tests | 215 |

Semiparametric Models | 358 |

433 | |

439 | |

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### Common terms and phrases

alternative apply approximation argument assume asymptotically normal bounded Chapter closed consider consistent constant contained continuous converges converges in distribution corresponding defined definition density depends derivative difference differentiable display distribution function efficient empirical equal equation equivalent Example exists expectation experiment finite fixed follows given gives hence independent inequality influence function instance integral interval known lemma likelihood ratio limit limit distribution linear matrix maximal maximum likelihood estimator mean measurable method null hypothesis observations obtain optimal parameter possible preceding probability problem projection proof prove random range rank relative respect sample satisfies score function sequence Show side space standard statistic subset sufficiently Suppose takes term theorem true uniform uniformly values variables variance vector yields zero