Includes bibliographical references (pages 267-273) and index.
Contents:
5.1. 1.1. Gaussian Shift Experiments 1.2. Estimator Sequences, Asymptotics of Information Bounds -- 1.3. Heuristics on Maximum Likelihood Estimator Sequences -- 1.4. Consistency of ML Estimators via Hellinger Distances -- 2.1. Stochastic Processes with Paths in Lp(T, T, [\U+00b5\]) -- 2.2. Minimum Distance Estimator Sequences -- 2.3. Some Comments on Gaussian Processes -- 2.4. Asymptotic Normality for Minimum Distance Estimator Sequences -- 3.1. Le Cam's First and Third Lemma -- 3.2. Proofs for Section 3.1 and some Variants -- 4.1. Lr-differentiable Statistical Models -- 4.2. Le Cam's Second Lemma for i.i.d. Observations -- 5.1. Gaussian Shift Experiments 9.4. One-dimensional Diffusions. 6.1. Quadratic and Mixed Normal Experiments -- 6.2. Likelihood Ratio Processes in Diffusion Models -- 6.3. Time Changes for Brownian Motion with Unknown Drift -- 7.1. Local Asymptotics of Type LAN, LAMN, LAQ -- 7.2. Asymptotic optimality of estimators in the LAN or LAMN setting -- 7.3. Le Cam's One-step Modification of Estimators -- 7.4. The Case of i.i.d. Observations -- 8.1. Ornstein-Uhlenbeck Process with Unknown Parameter Observed over a Long Time Interval -- 8.2. A Null Recurrent Diffusion Model -- 8.3. Some Further Remarks -- 9.1. Convergence of Martingales -- 9.2. Harris Recurrent Markov Processes -- 9.3. Checking the Harris Condition -- 9.4. One-dimensional Diffusions.
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