Foulley JL (1993) A simple argument showing how to derive restricted maximum likeli- hood. J Dairy Sci 76, 2320-2324Foulley, J. L. 1993. A simple argument showing how to derive restricted maximum likelihood. J.
5.1.1. Selection of estimator A recommended way of deciding on which statistical estimator should be used is to analyze individual level data and descriptive statistics across relevant study variables (Berlin, Williams, & Parra, 2014). For example, if data are continuous, maximum likelihood (ML)...
To answer this research question, we draw on both research in representation and psychology. Based on a large and diverse body of literature, we derive 25 different possible ways of asking people whether they feel represented. Following exposure to constructed representative claims by unelected repres...
A jacknife over loci was used to obtain confidence intervals for S. As a second measure of S, we used the maximum likelihood estimator developed by Enjalbert and David (2000). This method uses multi- locus individual heterozygosity and provides selfing rate estimates and confidence intervals ...
instrumental variables, the relationship between CSP and corporate financial performance either disappears or turns negative. Lahouel et al. (2019) employ the dynamic Generalized Method of Moments (GMM) estimator to handle the endogeneity problem and find that CSP has an insignificant impact on ...
that the standardised breakfast meals were associated with morning alertness differently based on their macronutrient composition should not be used solely to derive conclusions on recommending absolute quantities of a single macronutrient (e.g. the simplistic notion that consuming more grams of carbohydrate...
How to find asymptotic variance of MLE? Suppose that X_1,..., X_n form a random sample from a Uniform distribution interval (0,2\theta + 1) for some unknown parameter \theta is greater than -1/2. Compute the maximum likelihood estimator ( ...
How can data provide information to evaluate quality patient outcomes? Consider the regression model Y_i = beta_0 + beta_1 X_i + u_i. a) Suppose you know that beta_0 = 0. Derive a formula for the least-squares estimator of beta_1. b) Show your estimator from part (a) is Comp...
We derive novel, exact expressions for the mean instantaneous encounter rate of OU and RBM processes to quantify how empirically supported departures from classical encounter assumptions change encounter rates, and supplement these with numerical results where necessary. We outline the conditions under ...
This is different from the maximum likelihood estimator, which does not make use of Bessel's correction (i.e. dividing by (m − 1) instead of m). Our choice is driven by the fact that in practice we use a very small m, making the bias of the maximum likelihood estimator rather lar...