{"product_id":"9781506315904","title":"Maximum Likelihood Estimation: Logic and Practice","description":"\u003cp\u003eIn this volume the underlying logic and practice of maximum likelihood (ML) estimation is made clear by providing a general modeling framework that utilizes the tools of ML methods. This framework offers readers a flexible modeling strategy since it accommodates cases from the simplest linear models to the most complex nonlinear models that link a system of endogenous and exogenous variables with non-normal distributions. Using examples to illustrate the techniques of finding ML estimators and estimates, Eliason discusses: what properties are desirable in an estimator; basic techniques for finding ML solutions; the general form of the covariance matrix for ML estimates; the sampling distribution of ML estimators; the application of ML in the normal distribution as well as in other useful distributions; and some helpful illustrations of likelihoods. \u003c\/p\u003e","brand":"SAGE Publications","offers":[{"title":"Default Title","offer_id":47146043277552,"sku":"9781506315904","price":38.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0737\/7593\/9824\/files\/9781506315904_p0.jpg?v=1763718069","url":"https:\/\/shop-qa.barnesandnoble.com\/products\/9781506315904","provider":"Barnes \u0026 Noble (DEV)","version":"1.0","type":"link"}