A Mixed Model for Double Checking Fallible Auditors

This article discusses the problem of a fallible auditor who assesses the values of sampled records, but may make mistakes. To detect these mistakes, a subsample of the checked elements is checked again, now by an infallible expert.

We propose a model for this kind of double check, which takes into account that records are often correct; however, if they are incorrect, the errors can take on many different values—as is often the case in audit practice. The model therefore involves error probabilities as well as distributional parameters for error sizes.

We derive maximum likelihood estimators for these model parameters and derive from them an estimator for the mean size of the errors in the population. A simulation study shows that the latter outperforms some other—previously introduced—estimators.     

Multivariate Linear Regression Model with Elliptically Contoured Distributed Errors and Monotone Missing Dependent Variables

In this article, the multivariate linear regression model is studied under the assumptions that the error term of this model is described by the elliptically contoured distribution and the observations on the response variables are of a monotone missing pattern. It is primarily concerned with estimation of the model parameters, as well as with the development of the likelihood ratio test in order to examine the existence of linear constraints on the regression coefficients. An illustrative example is presented for the explanation of the results.     

A Bayesian model for measurement and misclassification errors alongside missing data,with an application to higher education participation in Australia

In this paper we consider the impact of both missing data and measurement errors on a longitudinal analysis of participation in higher education in Australia. We develop a general method for handling both discrete and continuous measurement errors that also allows for the incorporation of missing values and random effects in both binary and continuous response multilevel models. Measurement errors are allowed to be mutually dependent and their distribution may depend on further covariates. We show that our methodology works via two simple simulation studies. We then consider the impact of our measurement error assumptions on the analysis of the real data set.     

A Simple Derivation of Deletion Diagnostic Results for the General Linear Model with Correlated Errors

A general, simple and intuitive derivation is provided for diagnostics associated with the deletion of arbitrary subsets for the linear model with general covariance structure. These are seen to be most simply expressed, even for the well-studied case of independent and identically distributed data, in terms of a residual known variously as the conditional residual, the deletion prediction residual and the cross-validation residual. Particularly simple specializations arise when the subsets are of size 1 and of size 2, but the method is easy to apply for all subsets and to conditional deletions.