How School Contexts Shape the Relations Among Adolescents’ Beliefs,Peer Victimization,and Depressive Symptoms

The present research examined how school contexts shape the extent to which beliefs about the potential for change (implicit theories) interact with social adversity to predict depressive symptoms. A preregistered multilevel regression analysis using data from 6,237 ninth‐grade adolescents in 25 U.S. high schools showed a three‐way interaction: Implicit theories moderated the associations between victimization and depressive symptoms only in schools with high levels of school‐level victimization, but not in schools with low victimization levels. In high‐victimization schools, adolescents who believed that people cannot change (an entity theory of personality) were more depressed when they were victimized more frequently. Thus, the mental health correlates of adolescents’ implicit theories depend on both personal experiences and the norms in the context.     

Infant screen exposure links to toddlers' inhibition,but not other EF constructs: A propensity score study

Technology is pervasive in homes of families with young children, despite evidence for negative associations between infant exposure to screen‐based media and cognitive development that has led the American Academy of Pediatrics (AAP) to discourage parents from exposing children under the age of 18 months to any kind of screen time (AAP, 2016). Here, we apply a propensity score matching approach to estimate relations between electronic screen‐based media use in infancy and executive function in early toddlerhood. In an international sample of 416 firstborn infants, parental report of regular exposure to screen‐based media at 4 months predicted poorer performance on a test of inhibition at 14 months, but was unrelated to either cognitive flexibility or working memory at 14 months. Results of this study are therefore consistent with the view that early exposure to screen‐based media adversely affects the development of executive function.     

Sensitivity Analysis in Linear Programming Models with Common Inputs

The importance of sensitivity analysis information in linear programming has been stressed in the management science literature for some time. Indeed, Gal [3] has devoted an entire text to just this issue. Linear programs with common inputs (cost coefficients or right-hand-side values) present a problem in that classical sensitivity analysis does not allow for the simultaneous changes required to determine the sensitivity of these models to common inputs. We first survey the approaches previously developed for simultaneous-change sensitivity analysis and cast them in the framework of the special common input case. These general techniques are compared to a simple aggregate variable technique that has not received attention in the literature.     

On the power of goodness-of-fit tests for the exponential distribution under progressive Type-II censoring

The aim of this article is to review existing goodness-of-fit tests for the exponential distribution under progressive Type-II censoring and to provide some new ideas and adjustments. In particular, we consider two-parameter exponentially distributed random variables and adapt the proposed test procedures to our scenario if necessary. Then, we compare their power by an extensive simulation study. Furthermore, we propose five new test procedures that provide reasonable alternatives to those already known.     

Checking validity of monotone domain mean estimators

Estimates of population characteristics such as domain means are often expected to follow monotonicity assumptions. Recently, a method to adaptively pool neighbouring domains was proposed, which ensures that the resulting domain mean estimates follow monotone constraints. The method leads to asymptotically valid estimation and inference, and can lead to substantial improvements in efficiency, in comparison with unconstrained domain estimators. However, assuming incorrect shape constraints may lead to biased estimators. Here, we develop the Cone Information Criterion for Survey Data as a diagnostic method to measure monotonicity departures on population domain means. We show that the criterion leads to a consistent methodology that makes an asymptotically correct decision choosing between unconstrained and constrained domain mean estimators. The Canadian Journal of Statistics 47: 315–331; 2019 © 2019 Statistical Society of Canada     

OLS and IV estimation of regression models including endogenous interaction terms

We analyze a class of linear regression models including interactions of endogenous regressors and exogenous covariates. We show how to generate instrumental variables using the nonlinear functional form of the structural equation when traditional excluded instruments are unknown. We propose to use these instruments with identification robust IV inference. We furthermore show that, whenever functional form identification is not valid, the ordinary least squares (OLS) estimator of the coefficient of the interaction term is consistent and standard OLS inference applies. Using our alternative empirical methods we confirm recent empirical findings on the nonlinear causal relation between financial development and economic growth.     

On approximations via convolution-defined mixture models

An often-cited fact regarding mixing or mixture distributions is that their density functions are able to approximate the density function of any unknown distribution to arbitrary degrees of accuracy, provided that the mixing or mixture distribution is sufficiently complex. This fact is often not made concrete. We investigate and review theorems that provide approximation bounds for mixing distributions. Connections between the approximation bounds of mixing distributions and estimation bounds for the maximum likelihood estimator of finite mixtures of location-scale distributions are reviewed.     

Semiparametric sieve maximum likelihood estimation under cure model with partly interval censored and left truncated data for application to spontaneous abortion

Lifetime Data Analysis - This work was motivated by observational studies in pregnancy with spontaneous abortion (SAB) as outcome. Clearly some women experience the SAB event but the rest do not....     

A reflected feature space for CART

We present an algorithm for learning oblique decision trees, called HHCART(G). Our decision tree combines learning concepts from two classification trees, HHCART and Geometric Decision Tree (GDT). HHCART(G) is a simplified HHCART algorithm that uses linear structure in the training examples, captured by a modified GDT angle bisector, to define splitting directions. At each node, we reflect the training examples with respect to the modified angle bisector to align this linear structure with the coordinate axes. Searching axis parallel splits in this reflected feature space provides an efficient and effective way of finding oblique splits in the original feature space. Our method is much simpler than HHCART because it only considers one reflected feature space for node splitting. HHCART considers multiple reflected feature spaces for node splitting making it more computationally intensive to build. Experimental results show that HHCART(G) is an effective classifier, producing compact trees with similar or better results than several other decision trees, including GDT and HHCART trees.