The impact of parental illness on children’s schooling and labour force participation: evidence from Vietnam

Review of Economics of the Household - This paper investigates the relationship between parental illness and children’s engagement in education and labour market, using a panel data from...     

The Value Shift of the Russian Greens

The Green movement in the USSR/Russia has existed for more than forty years. During this period, seven groups have been shaped and consolidated within the movement (the conservationists, the alternativists, the traditionalists, the civil initiatives, the ecopoliticians, the ecopatriots, and the ecotechnocrats). The aim of this article is to consider the value shift each group underwent during the decade 1992–2001 within the context of the broader changes in the Russian society. The article emphasizes key influences conditioning the transformations of values. These include changing of local and national contexts caused by the overall process of society's westernization and globalization, as well as by the changes in each group's positions vis-a-vis the state, market economy, science and local population.     

Approaching in Distribution with Applications to Resampling of Stochastic Processes

We introduce the notion of weak approaching and conditionally weak approaching sequences of random processes. This notion generalizes the conventional weak convergence, and has been proposed for real valued random variables in Belyaev (1995). Some of the standard tools for an investigation of the behaviour of weak approaching sequences of random elements in metric spaces are developed. The spaces of smoothed and right-continuous functions with left-hand limits are considered. This technique allows us to use the resampling approach for an evaluation of distributions of continuous functionals on realizations of sum of an increasing number of independent random processes. Two numerical examples are presented for such functionals as supremum and number of level crossings.     

Bootstrap estimation of the variance of the error term in monotonic regression models

The variance of the error term in ordinary regression models and linear smoothers is usually estimated by adjusting the average squared residual for the trace of the smoothing matrix (the degrees of freedom of the predicted response). However, other types of variance estimators are needed when using monotonic regression (MR) models, which are particularly suitable for estimating response functions with pronounced thresholds. Here, we propose a simple bootstrap estimator to compensate for the over-fitting that occurs when MR models are estimated from empirical data. Furthermore, we show that, in the case of one or two predictors, the performance of this estimator can be enhanced by introducing adjustment factors that take into account the slope of the response function and characteristics of the distribution of the explanatory variables. Extensive simulations show that our estimators perform satisfactorily for a great variety of monotonic functions and error distributions.     

Optimal solutions to minimum total energy broadcasting problem in wireless ad hoc networks

In this paper, we present three schemes to solve minimum total energy broadcasting problem in wireless ad hoc networks based on an efficient IP (integer programming) subproblem technique. Due to its NP-hardness, many heuristics have been studied. However, the heuristics in the literature suffer from coarse performance ratio. It is important to have knowledge of the optimal solution structure in order to develop more efficient heuristics and algorithms. We present one IP formulation and two iterative algorithms which make use of relaxed IP's to solve subproblems. The computational results show that our approaches outperform other techniques in the literature. Research is partially supported by NSF and Air Force grants.     

Ukrainian migration in transition

Ukraine's actual role in the new frame of international migration has yet to be defined. It is likely that the country will become both donor and recipient of migrants. The intensity of Ukraine's involvement in migratory movements will depend on the complex interplay of socio‐economic, political, ecological and foreign policy factors. Three basic scenarios are outlined.     

Estimation of entropy-type integral functionals

ABSTRACT

Entropy-type integral functionals of densities are widely used in mathematical statistics, information theory, and computer science. Examples include measures of closeness between distributions (e.g., density power divergence) and uncertainty characteristics for a random variable (e.g., Rényi entropy). In this paper, we study U-statistic estimators for a class of such functionals. The estimators are based on ε-close vector observations in the corresponding independent and identically distributed samples. We prove asymptotic properties of the estimators (consistency and asymptotic normality) under mild integrability and smoothness conditions for the densities. The results can be applied in diverse problems in mathematical statistics and computer science (e.g., distribution identification problems, approximate matching for random databases, two-sample problems).     

Inequality and Unemployment in a Global Economy

This paper develops a new framework for examining the determinants of wage distributions that emphasizes within‐industry reallocation, labor market frictions, and differences in workforce composition across firms. More productive firms pay higher wages and exporting increases the wage paid by a firm with a given productivity. The opening of trade enhances wage inequality and can either raise or reduce unemployment. While wage inequality is higher in a trade equilibrium than in autarky, gradual trade liberalization first increases and later decreases inequality.     

Selective support vector machines

In this study we introduce a generalized support vector classification problem: Let X _i , i=1,…,n be mutually exclusive sets of pattern vectors such that all pattern vectors x _i,k , k=1,…,|X _i | have the same class label y _i . Select only one pattern vector $x_{i,k^{*}}In this study we introduce a generalized support vector classification problem: Let X _i , i=1,…,n be mutually exclusive sets of pattern vectors such that all pattern vectors x _i,k , k=1,…,|X _i | have the same class label y _i . Select only one pattern vector from each set X _i such that the margin between the set of selected positive and negative pattern vectors are maximized. This problem is formulated as a quadratic mixed 0-1 programming problem, which is a generalization of the standard support vector classifiers. The quadratic mixed 0-1 formulation is shown to be -hard. An alternative approach is proposed with the free slack concept. Primal and dual formulations are introduced for linear and nonlinear classification. These formulations provide flexibility to the separating hyperplane to identify the pattern vectors with large margin. Iterative elimination and direct selection methods are developed to select such pattern vectors using the alternative formulations. These methods are compared with a na?ve method on simulated data. The iterative elimination method is also applied to neural data from a visuomotor categorical discrimination task to classify highly cognitive brain activities.