Why spouses depress each other: A cross‐national study to test the shared resource hypothesis in depressive symptom concordance within older adult couples

The shared resource hypothesis suggests that married couples share the same environmental resources, which shape their health concordance. This study tests its cross‐national applicability. Cross‐sectional 2012–2013 Health and Retirement Study data from China, England, Mexico, and the United States were analyzed. Heterosexual couples (age ≥60) who were married or partnered were studied (N = 20,565 pairs). Dyadic data were analyzed by multilevel models to examine the effect of self and spousal social and physical health statuses on depressive symptoms. Regression models were used to test the relationship between couples' shared resources and depressive symptom concordance. Results indicated both husbands and wives' depressive symptoms were associated with their own and spouses' social and health statuses. Most couple‐level resources were insignificant predictors for Chinese and Mexican couples' concordance, but having more social and financial resources was associated with higher concordance among British and American couples. Self‐reported health was the most consistent predictor in all countries. The shared resource hypothesis was more applicable to depressive symptom concordance within couples in the United States and England, but not in China and Mexico. Couple‐centered intervention is suggested for clinical practice, and the spousal effect should be considered in policymaking.     

从跨体式新闻语言看新闻报道语言风格的形成

跨体式新闻语言是一种由不同语体标记相互渗透所形成的言语现象。特定社会的时代精神、文化心理、思维模式以及新闻记者的角色定位、受众的知识文化背景等语境因素,不但影响着跨体式新闻语言的生成,促使跨体式新闻语言生成某种特殊的修辞效果,而且还因该效果所呈现的风格色彩进一步影响到新闻报道语言风格的形成。     

Use of log−log survival function in modeling time-covariate interactions in Cox regression

Plotting of log−log survival functions against time for different categories or combinations of categories of covariates is perhaps the easiest and most commonly used graphical tool for checking proportional hazards (PH) assumption. One problem in the utilization of the technique is that the covariates need to be categorical or made categorical through appropriate grouping of the continuous covariates. Subjectivity in the decision making on the basis of eye-judgment of the plots and frequent inconclusiveness arising in situations where the number of categories and/or covariates gets larger are among other limitations of this technique. This paper proposes a non-graphical (numerical) test of the PH assumption that makes use of log−log survival function. The test enables checking proportionality for categorical as well as continuous covariates and overcomes the other limitations of the graphical method. Observed power and size of the test are compared to some other tests of its kind through simulation experiments. Simulations demonstrate that the proposed test is more powerful than some of the most sensitive tests in the literature in a wide range of survival situations. An example of the test is given using the widely used gastric cancer data.     

On distribution-weighted partial least squares with diverging number of highly correlated predictors

Summary.  Because highly correlated data arise from many scientific fields, we investigate parameter estimation in a semiparametric regression model with diverging number of predictors that are highly correlated. For this, we first develop a distribution-weighted least squares estimator that can recover directions in the central subspace, then use the distribution-weighted least squares estimator as a seed vector and project it onto a Krylov space by partial least squares to avoid computing the inverse of the covariance of predictors. Thus, distrbution-weighted partial least squares can handle the cases with high dimensional and highly correlated predictors. Furthermore, we also suggest an iterative algorithm for obtaining a better initial value before implementing partial least squares. For theoretical investigation, we obtain strong consistency and asymptotic normality when the dimension p of predictors is of convergence rate O { n ^1/2/ log ( n )} and o ( n ^1/3) respectively where n is the sample size. When there are no other constraints on the covariance of predictors, the rates n ^1/2 and n ^1/3 are optimal. We also propose a Bayesian information criterion type of criterion to estimate the dimension of the Krylov space in the partial least squares procedure. Illustrative examples with a real data set and comprehensive simulations demonstrate that the method is robust to non-ellipticity and works well even in 'small n –large p ' problems.     

The additive hazards model with high-dimensional regressors

This paper considers estimation and prediction in the Aalen additive hazards model in the case where the covariate vector is high-dimensional such as gene expression measurements. Some form of dimension reduction of the covariate space is needed to obtain useful statistical analyses. We study the partial least squares regression method. It turns out that it is naturally adapted to this setting via the so-called Krylov sequence. The resulting PLS estimator is shown to be consistent provided that the number of terms included is taken to be equal to the number of relevant components in the regression model. A standard PLS algorithm can also be constructed, but it turns out that the resulting predictor can only be related to the original covariates via time-dependent coefficients. The methods are applied to a breast cancer data set with gene expression recordings and to the well known primary biliary cirrhosis clinical data.     

Accelerated test system strength models based on Birnbaum–Saunders distribution: a complete Bayesian analysis and comparison

Several models for studies related to tensile strength of materials are proposed in the literature where the size or length component has been taken to be an important factor for studying the specimens’ failure behaviour. An important model, developed on the basis of cumulative damage approach, is the three-parameter extension of the Birnbaum–Saunders fatigue model that incorporates size of the specimen as an additional variable. This model is a strong competitor of the commonly used Weibull model and stands better than the traditional models, which do not incorporate the size effect. The paper considers two such cumulative damage models, checks their compatibility with a real dataset, compares them with some of the recent toolkits, and finally recommends a model, which appears an appropriate one. Throughout the study is Bayesian based on Markov chain Monte Carlo simulation.     

Women's “Justification” of Domestic Violence in Egypt

We explored the influences of women's social learning, marital resources and constraints, and exposure to norms about women's family roles on their views about wife hitting or beating among 5,450 participants in the 2005 Egypt Demographic and Health Survey. One half justified wife hitting or beating for some reason. Women from rural areas who were exposed to domestic violence more often justified such acts. Dependent wives whose husbands had more schooling, were blood relatives, and were coresident more often justified such acts. In settings where women tended to marry at older ages, women less often justified such acts. Women's resources and constraints in marriage accounted for the largest share of the variability in their attitudes about domestic violence against women.     

量子线中施主杂质的光致电离截面

本文就几种不同的线宽计算了准一维GaAs量子线中施主杂质的光致电离截面,同时也研究了磁场对光谱的影响。     

Study of partial least squares and ridge regression methods

This article considers both Partial Least Squares (PLS) and Ridge Regression (RR) methods to combat multicollinearity problem. A simulation study has been conducted to compare their performances with respect to Ordinary Least Squares (OLS). With varying degrees of multicollinearity, it is found that both, PLS and RR, estimators produce significant reductions in the Mean Square Error (MSE) and Prediction Mean Square Error (PMSE) over OLS. However, from the simulation study it is evident that the RR performs better when the error variance is large and the PLS estimator achieves its best results when the model includes more variables. However, the advantage of the ridge regression method over PLS is that it can provide the 95% confidence interval for the regression coefficients while PLS cannot.     

Eliciting Subjective Survival Curves: Lessons from Partial Identification

When analyzing data on subjective expectations of continuous outcomes, researchers have access to a limited number of reported probabilities for each respondent from which to construct complete distribution functions. Moreover, reported probabilities may be rounded and thus not equal to true beliefs. Using survival expectations elicited from a representative sample from the Netherlands, we investigate what can be learned if we take these two sources of missing information into account and expectations are therefore only partially identified. We find novel evidence for rounding by checking whether reported expectations are consistent with a hazard of death that increases weakly with age. Only 39% of reported beliefs are consistent with this under the assumption that all probabilities are reported precisely, while 92% are if we allow for rounding. Using the available information to construct bounds on subjective life expectancy, we show that the data alone are not sufficiently informative to allow for useful inference in partially identified linear models, even in the absence of rounding. We propose to improve precision by interpolation between rounded probabilities. Interpolation in combination with a limited amount of rounding does yield informative intervals.