Statistical power in two-level hierarchical linear models with arbitrary number of factor levels

on some bayesian statistical models in actuarial science with emphasis on claim count

چکیده ندارد.

A Flexible Method for Conducting Power Analysis for Two- and Three-Level Hierarchical Linear Models in R

Just-identified versus overidentified two-level hierarchical linear models with missing data.

The development of model-based methods for incomplete data has been a seminal contribution to statistical practice. Under the assumption of ignorable missingness, one estimates the joint distribution of the complete data for thetainTheta from the incomplete or observed data y(obs). Many interesting models involve one-to-one transformations of theta. For example, with y(i) approximately N(mu, Si...

New Approach in Fitting Linear Regression Models with the Aim of Improving Accuracy and Power

The main contribution of this work lies in challenging the common practice of inferential statistics in the realm of simple linear regression for attaining a higher degree of accuracy when multiple observations are available, at least, at one level of the regressor variable. We derive sufficient conditions under which one can improve the accuracy of the interval estimations at quite affordable ...

Two-Level Optimization Problems with Infinite Number of Convex Lower Level Constraints

‎This paper proposes a new form of optimization problem which is a two-level programming problem with infinitely many lower level constraints‎. ‎Firstly‎, ‎we consider some lower level constraint qualifications (CQs) for this problem‎. ‎Then‎, ‎under these CQs‎, ‎we derive formula for estimating the subdifferential of its valued function‎. ‎Finally‎, ‎we present some necessary optimality condit...