Abstract and Keywords
This chapter provides an overview of methods for estimating parameters and standard errors. Because it is impossible to cover all statistical estimation methods in this chapter, we focus on those approaches that are of general interest and are frequently used in social science research. For each estimation method, the properties of the estimator are highlighted under idealized conditions; drawbacks potentially resulting from violations of ideal conditions are also discussed. In addition, the chapter reviews several widely used computational algorithms for calculating parameter estimates.
Keywords: Maximum likelihood, pseudo-maximum likelihood, generalized least squares, robust M-estimators, Bayes methods, estimating equations, δ-method, bootstrap, Newton algorithm, EM algorithm, Markov chain Monte Carlo
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