The Past in the Present. Lessons on Semiotics of History from George H. Mead and Boris A. Uspensky

This article examines the parallels between George H. Mead's theory of time and Boris A. Uspensky's semiotics of history, looking for implications relevant to the symbolic interactionist theory of historical processes. It suggests that Mead's theory of time and theory of communication hold important implications for semiotic analysis of the historical dimension of sociocultural phenomena. A further aim is to link the symbolic interactionist tradition of the Chicago school with the Tartu school and its semiotics of history. This would fuel the further development of both theoretical orientations.     

Conditions for Exercising Residents’ Voting Rights in Long-Term Care Residences: A Prospective Multicenter Study

To assess voting conditions in long-term care settings, we conducted a multicenter survey after the 2009 European elections in France. A questionnaire about voting procedures and European elections was proposed in 146 out of 884 randomized facilities. Sixty-four percent of facilities answered the questionnaire. Four percent of residents voted (national turnout: 40%), by proxy (58%) or at polling places (42%). Abstention related to procedural issues was reported in 32% of facilities. Sixty-seven percent of establishments had voting procedures, and 53% declared that they assessed residents’ capacity to vote. Assistance was proposed to residents for voter registration, for proxy voting, and for voting at polling places, respectively, in 33%, 87%, and 80% of facilities. This survey suggests that residents may be disenfranchised and that more progress should be made to protect the voting rights of residents in long-term care facilities.     

Multidimensional Almost Dominance: Child Wellbeing in Egypt

A major drawback of First Order Stochastic Dominance approach is dominance indetermination. Levy and Leshno in 2002 suggested Almost Stochastic Dominance as a remedy in the uni-dimensional case. We introduce a Generalization of Almost First and second Order Dominance (MAFOD and MASOD) to the multidimensional case with application on child wellbeing in Egypt. We perform a multidimensional (FOD) analysis on seven deprivation indicators for three age-groups of children from Egypt 2014 Demographic and Health Survey (EDHS14). This methodology allows the ordinal ranking of regions and governorates of Egypt in terms of their children wellbeing based on their probability of domination. To solve the dominance indetermination we apply MAFOD and MASOD.     

On the Multivariate Two-Sample Problem Using Strong Approximations of Empirical Copula Processes

In this article, we establish optimal rates for the strong approximation of empirical copula processes in ?² by sequences of Gaussian processes. These results are applied to investigate Cramér–von Mises-type statistics.     

Consistency of information criteria for model selection with missing data

ABSTRACT

In this paper, we investigate the consistency of the Expectation Maximization (EM) algorithm-based information criteria for model selection with missing data. The criteria correspond to a penalization of the conditional expectation of the complete data log-likelihood given the observed data and with respect to the missing data conditional density. We present asymptotic properties related to maximum likelihood estimation in the presence of incomplete data and we provide sufficient conditions for the consistency of model selection by minimizing the information criteria. Their finite sample performance is illustrated through simulation and real data studies.     

Multiple nonlinear regression of the Markovian arrival process for estimating the daily global solar radiation

Abstract

Solar radiation is a global ecological phenomenon that affects life everywhere. In this study, a new statistical method, called the Quartiles-Moment's method, is proposed to estimate the scale and shape parameters of the exponentiated Gumbel maximum distribution (EGMD). The Kolomogorov–Smirnov test and the percentiles of the dataset are thus used to fit the dataset of the daily global solar radiation and the corresponding daily maximum temperature with EGMD. Thence, multiple nonlinear regression of the daily global solar radiation and the corresponding daily maximum temperature are produced and compared with the real dataset accordingly.     

A Combined Nonlinear Programming Model and Kibria Method for Choosing Ridge Parameter Regression

Ridge regression solves multicollinearity problems by introducing a biasing parameter that is called ridge parameter; it shrinks the estimates as well as their standard errors in order to reach acceptable results. Many methods are available for estimating a ridge parameter. This article has considered some of these methods and also proposed a combined nonlinear programming model and Kibria method. A simulation study has been made to evaluate the performance of the proposed estimators based on the minimum mean squared error criterion. The simulation study indicates that under certain conditions the proposed estimators outperform the least squares (LS) estimators and other popular existing estimators. Moreover, the new proposed model is applied on dataset that suffers also from the presence of heteroscedastic errors.     

Conceptualization and Measurement of Adolescent Prosocial Behavior: Looking Back and Moving Forward

The struggle to cast a net around the numerous ways prosocial behavior is expressed lends itself to the absence of widely accepted methods of measurement. Additionally, research intent on evaluating the psychometric properties of current approaches has been somewhat limited. Weaving together seminal as well as contemporary research, the current review focuses on how these conceptual and measurement issues pertain to adolescent studies (in an intentional effort to offset the somewhat disproportionate focus directed toward prosocial development in infants, children, and adults). Recommendations to address current limitations and attain a more nuanced understanding of the construct are presented and discussed.     

Soil organic carbon stock in different urban land uses: high stock evidence in urban parks

Urban Ecosystems - Urban areas are major producers of atmospheric CO2 emissions, but at the same time they can offset some of the associated C losses by retaining stable organic carbon in their...     

Non‐parametric Estimation of Extreme Risk Measures from Conditional Heavy‐tailed Distributions

In this paper, we introduce a new risk measure, the so‐called conditional tail moment. It is defined as the moment of order a ≥ 0 of the loss distribution above the upper α‐quantile where α ∈ (0,1). Estimating the conditional tail moment permits us to estimate all risk measures based on conditional moments such as conditional tail expectation, conditional value at risk or conditional tail variance. Here, we focus on the estimation of these risk measures in case of extreme losses (where α ↓0 is no longer fixed). It is moreover assumed that the loss distribution is heavy tailed and depends on a covariate. The estimation method thus combines non‐parametric kernel methods with extreme‐value statistics. The asymptotic distribution of the estimators is established, and their finite‐sample behaviour is illustrated both on simulated data and on a real data set of daily rainfalls.