Relational and Overt Aggression in Childhood and Adolescence: Clarifying Mean‐level Gender Differences and Associations with Peer Acceptance

Research on relational aggression has drawn attention to how girls may be likely to aggress, but the role of gender is not fully understood. There are opposing views regarding whether relational aggression is most common among girls. Current findings demonstrate that when gender differences in relational aggression are assessed with peer nominations, gender differences favoring girls are more likely: (1) in adolescence than childhood; and (2) when statistical overlap with overt aggression is controlled. Results also indicated that associations of relational aggression with peer acceptance depend on the aggressor's gender, the peer rater's gender, and whether overlap with overt aggression is controlled. Associations of relational aggression with lower acceptance became non‐significant when overt aggression was controlled, suggesting that relational aggression displayed in isolation may not damage acceptance. In fact, in mid‐adolescence, girls’ relational aggression predicted greater liking by boys. Reducing relational aggression among adolescent girls may be especially challenging if the behavior is linked with acceptance by boys.     

Choosing monetary sequences: theory and experimental evidence

We formulate and investigate experimentally a model of how individuals choose between time sequences of monetary outcomes. The model assumes that a decision maker uses, sequentially, two criteria to screen options. Each criterion only permits a decision between some pairs of options, while the other options are incomparable according to that criterion. When the first criterion is not decisive, the decision maker resorts to the second criterion to select an alternative. We find that: (1) traditional economic models based on discounting alone cannot explain a significant (almost 30%) proportion of the data no matter how much variability in the discount functions is allowed; (2) our model, despite considering only a specific (exponential) form of discounting, can explain the data much better solely thanks to the use of the secondary criterion; (3) our model explains certain specific patterns in the choices of the “irrational” people. We reject the hypothesis that anomalous behavior is due simply to random “mistakes” around the basic predictions of discounting theories: deviations are not random and there are clear systematic patterns of association between “irrational” choices.     

Population monotonic path schemes for simple games

A path scheme for a game is composed of a path, i.e., a sequence of coalitions that is formed during the coalition formation process and a scheme, i.e., a payoff vector for each coalition in the path. A path scheme is called population monotonic if a player’s payoff does not decrease as the path coalition grows. In this study, we focus on Shapley path schemes of simple games in which for every path coalition the Shapley value of the associated subgame provides the allocation at hand. Obviously, each Shapley path scheme of a game is population monotonic if and only if the Shapley allocation scheme of the game is population monotonic in the sense of Sprumont (Games Econ Behav 2:378–394, 1990). We prove that a simple game allows for population monotonic Shapley path schemes if and only if the game is balanced. Moreover, the Shapley path scheme of a specific path is population monotonic if and only if the first winning coalition that is formed along the path contains every minimal winning coalition. We also show that each Shapley path scheme of a simple game is population monotonic if and only if the set of veto players of the game is a winning coalition. Extensions of these results to other efficient probabilistic values are discussed.     

Pooling designs associated with unitary space and ratio efficiency comparison

Let mathbbF^(2n+d)_q²mathbb{F}^{(2nu+delta)}_{q^{2}} be a (2ν+δ)-dimensional unitary space of mathbbF_q²mathbb{F}_{q^{2}} , where δ=0 or 1. In this paper we construct a family of inclusion matrices associated with subspaces of mathbbF^(2n+d)_q²mathbb{F}^{(2nu+delta)}_{q^{2}} , and exhibit its disjunct property. Moreover, we compare the ratio efficiency of this construction with others, and find it smaller under some conditions.     

Effects of unmeasured heterogeneity in the linear transformation model for censored data

We investigate the effect of unobserved heterogeneity in the context of the linear transformation model for censored survival data in the clinical trials setting. The unobserved heterogeneity is represented by a frailty term, with unknown distribution, in the linear transformation model. The bias of the estimate under the assumption of no unobserved heterogeneity when it truly is present is obtained. We also derive the asymptotic relative efficiency of the estimate of treatment effect under the incorrect assumption of no unobserved heterogeneity. Additionally we investigate the loss of power for clinical trials that are designed assuming the model without frailty when, in fact, the model with frailty is true. Numerical studies under a proportional odds model show that the loss of efficiency and the loss of power can be substantial when the heterogeneity, as embodied by a frailty, is ignored. An erratum to this article can be found at     

Accuracy of approximate progressively censored reliability sampling plans for exponential models

Reliability sampling plans provide an efficient method to determine the acceptability of a product based upon the lifelengths of some test units. Usually, they depend on the producer and consumer’s quality requirements and do not admit closed form solutions. Acceptance sampling plans for one- and two-parameter exponential lifetime models, derived by approximating the operating characteristic curve, are presented in this paper. The accuracy of these approximate plans, which are explicitly expressible and valid for failure and progressive censoring, is assessed. The approximation proposed in the one-parameter case is found to be practically exact. Explicit lower and upper bounds on the smallest sample size are given in the two-parameter case. Some additional advantages are also pointed out.     

Estimating a sensitive proportion through randomized response procedures based on auxiliary information

Randomized response techniques are widely employed in surveys dealing with sensitive questions to ensure interviewee anonymity and reduce nonrespondents rates and biased responses. Since Warner’s (J Am Stat Assoc 60:63–69, 1965) pioneering work, many ingenious devices have been suggested to increase respondent’s privacy protection and to better estimate the proportion of people, π _A , bearing a sensitive attribute. In spite of the massive use of auxiliary information in the estimation of non-sensitive parameters, very few attempts have been made to improve randomization strategy performance when auxiliary variables are available. Moving from Zaizai’s (Model Assist Stat Appl 1:125–130, 2006) recent work, in this paper we provide a class of estimators for π _A , for a generic randomization scheme, when the mean of a supplementary non-sensitive variable is known. The minimum attainable variance bound of the class is obtained and the best estimator is also identified. We prove that the best estimator acts as a regression-type estimator which is at least as efficient as the corresponding estimator evaluated without allowing for the auxiliary variable. The general results are then applied to Warner and Simmons’ model.     

A permutation approach for testing heterogeneity in two-sample categorical variables

In many sciences researchers often meet the problem of establishing if the distribution of a categorical variable is more concentrated, or less heterogeneous, in population P ₁ than in population P ₂. An approximate nonparametric solution to this problem is discussed within the permutation context. Such a solution has similarities to that of testing for stochastic dominance, that is, of testing under order restrictions, for ordered categorical variables. Main properties of given solution and a Monte Carlo simulation in order to evaluate its degree of approximation and its power behaviour are examined. Two application examples are also discussed.