Georgios Afendras

Uniform integrability of the OLS estimators, and the convergence of their moments

The problem of convergence of moments of a sequence of random variables to the moments of its asymptotic distribution is important in many applications. These include the determination of the optimal training sample size in the cross-validation estimation of the generalization error of computer algorithms, and in the construction of graphical methods for studying dependence patterns between two biomarkers. In this paper, we prove the uniform integrability of the ordinary least squares estimators of a linear regression model, under suitable assumptions on the design matrix and the moments of the errors. Further, we prove the convergence of the moments of the estimators to the corresponding moments of their asymptotic distribution, and study the rate of the moment convergence. The canonical central limit theorem corresponds to the simplest linear regression model. We investigate the rate of the moment convergence in canonical central limit theorem proving a sharp improvement of von Bahr’s (Ann Math Stat 36:808–818, 1965) theorem.

Orthogonal polynomials in the cumulative Ord family and its application to variance bounds

ABSTRACT This article presents and reviews several basic properties of the Cumulative Ord family of distributions; this family contains all the commonly used discrete distributions. A complete classification of the Ord family of probability mass functions is related to the orthogonality of the corresponding Rodrigues polynomials. Also, for any random variable X of this family and for any suitable function g in , the article provides useful relationships between the Fourier coefficients of g (with respect to the orthonormal polynomial system associated to X) and the Fourier coefficients of the forward difference of g (with respect to another system of polynomials, orthonormal with respect to another distribution of the system). Finally, using these properties, a class of bounds for the variance of is obtained, in terms of the forward differences of g. These bounds unify and improve several existing results.

The Out-of-Source Error in Multi-Source Cross Validation-Type Procedures

A scientific phenomenon under study may often be manifested by data arising from processes, i.e. sources, that may describe this phenomenon. In this context of multi-source data, we define the “out-of-source” error, that is the error committed when a new observation of unknown source origin is allocated to one of the sources using a rule that is trained on the known labeled data. We present an unbiased estimator of this error, and discuss its variance. We derive natural and easily verifiable assumptions under which the consistency of our estimator is guaranteed for a broad class of loss functions and data distributions. Finally, we evaluate our theoretical results via a simulation study.

A factorial moment distance and an application to the matching problem@@@A factorial moment distance and an application to the matching problem

In this note we introduce the notion of factorial moment distance for non-negative integer-valued random variables and we compare it with the total variation distance. Furthermore, we study the rate of convergence in the classical matching problem and in a generalized matching distribution.