A game theoretic approach to measuring degree of centrality in social networks

We present a new measure of degree of centrality in a social network which is based on a natural extension of the Banzhaf (1965) index of power in an N-person game.     

Bayesian modeling of autoregressive partial linear models with scale mixture of normal errors

Normality and independence of error terms are typical assumptions for partial linear models. However, these assumptions may be unrealistic in many fields, such as economics, finance and biostatistics. In this paper, a Bayesian analysis for partial linear model with first-order autoregressive errors belonging to the class of the scale mixtures of normal distributions is studied in detail. The proposed model provides a useful generalization of the symmetrical linear regression model with independent errors, since the distribution of the error term covers both correlated and thick-tailed distributions, and has a convenient hierarchical representation allowing easy implementation of a Markov chain Monte Carlo scheme. In order to examine the robustness of the model against outlying and influential observations, a Bayesian case deletion influence diagnostics based on the Kullback–Leibler (K–L) divergence is presented. The proposed method is applied to monthly and daily returns of two Chilean companies.     

Partly linear models on Riemannian manifolds

In partly linear models, the dependence of the response y on (x ^T, t) is modeled through the relationship y=x ^T β+g(t)+?, where ? is independent of (x ^T, t). We are interested in developing an estimation procedure that allows us to combine the flexibility of the partly linear models, studied by several authors, but including some variables that belong to a non-Euclidean space. The motivating application of this paper deals with the explanation of the atmospheric SO₂ pollution incidents using these models when some of the predictive variables belong in a cylinder. In this paper, the estimators of β and g are constructed when the explanatory variables t take values on a Riemannian manifold and the asymptotic properties of the proposed estimators are obtained under suitable conditions. We illustrate the use of this estimation approach using an environmental data set and we explore the performance of the estimators through a simulation study.     

Welfare programs and labor supply in developing countries: experimental evidence from Latin America

This study looks at the effect of welfare programs on work incentives and the adult labor supply in developing countries. The analysis builds on the experimental evaluations of three programs implemented in rural areas: Mexico’s Programa Nacional de Educación, Salud y Alimentación (PROGRESA), Nicaragua’s Red de Protección Social, and Honduras’ Programa de Asignación Familiar. Comparable results for the three countries indicate that the effects that the programs have had on the labor supply of participating adults have been mostly negative but are nonetheless small and not statistically significant. However, the evidence does point to the presence of other effects on labor markets. In the case of PROGRESA, there is a small positive effect on the number of hours worked by female beneficiaries and a sizeable increase in wages among male beneficiaries and a resulting increase in household labor income. Moreover, PROGRESA seems to have reduced female labor-force participation in ineligible households. These results imply that large-scale interventions may have broader equilibrium effects.     

On the consistency of MLE in finite mixture models of exponential families

Finite mixtures of densities from an exponential family are frequently used in the statistical analysis of data. Modelling by finite mixtures of densities from different exponential families provide more flexibility in the fittings, and get better results. However, in mixture problems, the log-likelihood function very often does not have an upper bound and therefore a global maximum does not always exist. Redner and Walker (1984. Mixture densities, maximum likelihood and the EM algorithm. SIAM Rev. 26, 195–239) provide conditions to assure the existence, consistency and asymptotic normality of the maximum likelihood estimator.     

Post hoc false positive control for structured hypotheses

In a high-dimensional multiple testing framework, we present new confidence bounds on the false positives contained in subsets S of selected null hypotheses. These bounds are post hoc in the sense that the coverage probability holds simultaneously over all S, possibly chosen depending on the data. This article focuses on the common case of structured null hypotheses, for example, along a tree, a hierarchy, or geometrically (spatially or temporally). Following recent advances in post hoc inference, we build confidence bounds for some prespecified forest-structured subsets and deduce a bound for any subset S by interpolation. The proposed bounds are shown to improve substantially previous ones when the signal is locally structured. Our findings are supported both by theoretical results and numerical experiments. Moreover, our bounds can be obtained by an algorithm (with complexity bilinear in the sizes of the reference hierarchy and of the selected subset) that is implemented in the open-source R package sansSouci available from https://github.com/pneuvial/sanssouci , making our approach operational.     

Estimation of slowly time-varying trend function in long memory regression models

ABSTRACT

We study the asymptotic properties of the least-squares estimator for the trend function of a particular class of locally stationary models, which are defined by considering a smooth variation of the trend function. Additionally, errors are assumed to be realizations from a long-range dependent stationary Gaussian process. Our findings are then illustrated through Monte Carlo simulations.     

The Coming of Game Theory

This is a brief historical note on game theory. We cover its historical roots (prior to its formal definition in 1944), and look at its development until the late 1960's.     

FAMILY THERAPY AND IDEOLOGY*

Contemporary family therapy is examined in terms of its ideological content. The adoption of cybernetics and biological systems theory to the family has led to the erroneous belief that familytherapy is exclusively concerned with natural systems, is process oriented, content relative, and above ideology. This article argues that the family and the enterprise of family therapy are social systems and under the influence of the ideology particular to a given society. The strategic family therapy treatment of a family with a drug-addicted member serves as an example to clarify the ideological themes of contemporary family therapy. These themes are: (a) family reductionism and blaming; (b) the relabeling of personal injustice into communicational terminology; (c) the assumption that the nuclear family is optimal; (d) the conceptualization of therapy as a commodity; and (e) the model of human relatedness and intimacy supported by family therapy.