Maximum Likelihood Estimation for Dependent Observations 论文
1976Journal of the Royal Statistical Society Series B (Statistical Methodology)引用 249
Statistical Methods and InferenceBayesian Methods and Mixture ModelsStatistical Methods and Bayesian Inference
摘要
Summary The asymptotic properties of m.l.e. are discussed for generally dependent observations. Conditions are derived for weak consistency and asymptotic Normality of the estimates. We further consider the case where some of the parameters are “transient” in the sense that the accumulated information on them from the sample does not increase indefinitely; then the interest lies in estimating the other parameters consistently. Examples are given, and the work is related to that of Neyman and Scott (1948).