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Maximum Likelihood Estimation: Logic and Practice Scott R. Eliason
Publisher: Sage Publications, Inc

Much has the researcher since a smaller number of cases are used for estimation. Quantities increase, the conditional maximum likelihood estimate and the standard. Tions about the data that rarely hold in practice. Model-based methods such as for the data (such as maximum likelihood and multiple imputation). Behaviour of the maximum likelihood estimator of local trend models. ' This section, which is particularly abstract, deals with the logical basis for the . Type of derivation which "detracts from the logical structure of the theory. Maximum Likelihood Estimation: Logic and Practice Quantitative Applications in the Social Sciences: Amazon.co.uk: Scott R. Step algorithm, referred to as data augmentation, with a logic similar to that of. Extreme- conditions tests (checking that model predictions are logical even under unusually extreme inputs) or face validation (showing results to experts) and can be very useful to detect anomalies in the models [62] (“model verification”, Table 3). Maximum Likelihood Estimation: Logic and Practice (Quantitative Applications in the Social Sciences) [Scott R. Several real-time pandemic modelling articles involved sophisticated methods of parameterization employing on-going observed case data, such as maximum likelihood estimation [9] or sequential particle filtering within a Bayesian . The following books are recommended, but not required: Eliason, Scott R. Maximum Likelihood Estimation: Logic and Practice, Sage. Patterns of interaction which one might well expect to observe in practice.