preface 1 mathematical background 1.1 the concept of limit 1.2 embed sequences 1.3 infinite series 1.4 order relations and rates of convergence 1.5 continuity 1.6 distributions 1.7 problems 2 convergence in probability and in law 2.1 convergence in probability 2.2 applications 2.3 convergence in law 2.4 the central limit theorem 2.5 taylors theorem and the delta method 2.6 uniform convergence 2.7 the clt for independent non—identical random variables 2.8 central limit theorem for dependent variables 2.9 problems 3 performance of statistical tests 3.1 critical values 3.2 paring two treatments 3.3 power and sample size 3.4 parison of tests: relative efficiency 3.5 robustness 3.6 problems 4 estimation 4.1 confidence intervals 4.2 accuracy of point estimators 4.3 paring estimators 4.4 sampling from a finite population 4.5 problems 5 multivariate extensions 5.1 convergence of multivariate distributions. 5.2 the bivariate normal distribution 5.3 some linear algebra 5.4 the multivariate normal distribution 5.5 some applications 5.6 estimation and testing in 2 × 2 tables 5.7 testing goodness of fit 5.8 problems 6 nonparametric estimation 6.1 u—statistics 6.2 statistical functionals 6.3 limit distributions of statistical functionais 6.4 density estimation 6.5 bootstrapping 6.6 problems 7 efficient estimators and tests 7.1 mamum likelihood 7.2 fisher information 7.3 asymptotic normality and multiple roots 7.4 efficiency 7.5 the multiparameter case ⅰ.asymptotic norrnality 7.6 the multiparameter case ⅱ.efficiency 7.7 tests and confidence intervals 7.8 contingency tables 7.9 problems appendix references author index subject index
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