In this paper, we estimate the Shannon entropy S(f)=-E[log(f(x))] of a one-sided linear process with probability density function f(x). We employ the integral estimator Sn(f), which utilizes the standard kernel density estimator fn(x) of f(x). We show that Sn(f) converges to S(f) almost surely...

In this paper, we estimate the Shannon entropy S(f)=-E[log(f(x))] of a one-sided linear process with probability density function f(x). We employ the integral estimator Sn(f), which utilizes the standard kernel density estimator fn(x) of f(x). We show that Sn(f) converges to S(f) almost surely...

This paper introduces Information Theoretic - based methods for estimating a target variable in a set of small geographical areas, by exploring spatially heterogeneous relationships at the disaggregate level. Controlling for spatial effects means introducing models whereby the assumption is that...

This paper introduces Information Theoretic - based methods for estimating a target variable in a set of small geographical areas, by exploring spatially heterogeneous relationships at the disaggregate level. Controlling for spatial effects means introducing models whereby the assumption is that...

The standard gravity model of aggregate migration erroneously assumes homogeneity, quasi-symmetry, and (log) linearity. As a result, estimation of the model using, say, the convenient OLS estimator is plagued by omitted-variables and misspecification biases. This paper, therefore, presents...

The standard gravity model of aggregate migration erroneously assumes homogeneity, quasi-symmetry, and (log) linearity. As a result, estimation of the model using, say, the convenient OLS estimator is plagued by omitted-variables and misspecification biases. This paper, therefore, presents...

It is widely agreed that studying the relationship between school quality and academic achievement will benefit public investment in education. This is particularly true in Africa where, the 1990 World Conference on ‘Education for All’ led to renewed commitments to quality basic education....

A way to achieve feature selection for classification problems polluted by label noise is proposed. The performances of traditional feature selection algorithms often decrease sharply when some samples are wrongly labelled. A method based on a probabilistic label noise model combined with a...