Statistical inference is mainly concerned with providing some conclusions about the parameters which describe the distribution of a variable of interest in a certain population on the basis of a random sample. In this article, we review point estimation methods which consist of assigning a value to each unknown parameter. Basic properties of an estimator are illustrated together with the main methods of finding estimators: method of moments, maximum likelihood, and Bayesian methods. In particular, we discuss maximum likelihood estimation of the most well-known item response theory model, the Rasch model, and illustrate it through a data analysis example. © 2010 Elsevier Ltd. All rights reserved.
Bartolucci, F., Scrucca, L. (2010). Point estimation methods with applications to item response theory models. Oxford : Elsevier Ltd [10.1016/B978-0-08-044894-7.01376-2].
Point estimation methods with applications to item response theory models
Bartolucci F.;Scrucca L.
2010
Abstract
Statistical inference is mainly concerned with providing some conclusions about the parameters which describe the distribution of a variable of interest in a certain population on the basis of a random sample. In this article, we review point estimation methods which consist of assigning a value to each unknown parameter. Basic properties of an estimator are illustrated together with the main methods of finding estimators: method of moments, maximum likelihood, and Bayesian methods. In particular, we discuss maximum likelihood estimation of the most well-known item response theory model, the Rasch model, and illustrate it through a data analysis example. © 2010 Elsevier Ltd. All rights reserved.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
