A transformation approach for the analysis of interval-censored failure time data

This paper discusses the analysis of interval-censored failure time data, which has recently attracted a great amount of attention (Li and Pu, Lifetime Data Anal 9:57–70, 2003; Sun, The statistical analysis of interval-censored data, 2006; Tian and Cai, Biometrika 93(2):329–342, 2006; Zhang et al., Can J Stat 33:61–70, 2005). Interval-censored data mean that the survival time of interest is observed only to belong to an interval and they occur in many fields including clinical trials, demographical studies, medical follow-up studies, public health studies and tumorgenicity experiments. A major difficulty with the analysis of interval-censored data is that one has to deal with a censoring mechanism that involves two related variables. For the inference, we present a transformation approach that transforms general interval-censored data into current status data, for which one only needs to deal with one censoring variable and the inference is thus much easy. We apply this general idea to regression analysis of interval-censored data using the additive hazards model and numerical studies indicate that the method performs well for practical situations. An illustrative example is provided.     

$${\mathcal{L}}_p$$ loss functions: a robust bayesian approach

In bayesian inference, the Bayes estimator is the alternative with the minimum expected loss. In most cases, the loss function shows the distance between the alternative and the parameter. Therefore, any distance can lead to a loss function. Among the best known distance functions is L _p one, where the choice of value p may be difficult and arbitrary. This paper examines robust models where the loss function is modelled by family L _p . Our solution concept is the non-dominated alternative. We characterize the non-dominated set by having the posterior distribution function satisfy a particular asymmetry property. We also include an example to illustrate the methodology described.     

Optimal model averaging estimation for correlation structure in generalized estimating equations

Longitudinal data analysis requires a proper estimation of the within-cluster correlation structure in order to achieve efficient estimates of the regression parameters. When applying likelihood-based methods one may select an optimal correlation structure by the AIC or BIC. However, such information criteria are not applicable for estimating equation based approaches. In this paper we develop a model averaging approach to estimate the correlation matrix by a weighted sum of a group of patterned correlation matrices under the GEE framework. The optimal weight is determined by minimizing the difference between the weighted sum and a consistent yet inefficient estimator of the correlation structure. The computation of our proposed approach only involves a standard quadratic programming on top of the standard GEE procedure and can be easily implemented in practice. We provide theoretical justifications and extensive numerical simulations to support the application of the proposed estimator. A couple of well-known longitudinal data sets are revisited where we implement and illustrate our methodology.     

Canada is #IdleNoMore: exploring dynamics of Indigenous political and civic protest in the Twitterverse

Social media have been playing a growingly important role in grassroots protest over the last five years. While many scholars have explored dynamics of political cyberprotest (e.g., the ongoing transnational Occupy movement, the 2012 Quebec student strike, the student-led protest movement in Chile between 2011 and 2013), few have studied sub-dynamics relating to ethno-cultural minorities’ uses of social media to gain visibility, mobilize support, and engage in political and civil action. We fill part of this gap in the academic literature by investigating uses of Twitter for political engagement in the context of the Canada-based Idle No More movement (INM). This ongoing protest initiative, which emerged in December 2012, seeks to mobilize Indigenous Peoples in Canada and internationally as well as their non-Indigenous allies. It does so by bringing attention to their culture, struggles, and identities as well as advocating for changes in policy areas relating to the environment, governance, and socio-economic matters. Our study explores to what extent references to aspects of Indigenous identities and culture shaped INM-related tweeting and, by extension, activism during the summer of 2013. We conducted a quantitative and qualitative content analysis of 1650 #IdleNoMore tweets shared by supporters of this movement between 3 July 2013 and 2 August 2013. Our study demonstrates that unlike other social media-intensive movements where economic and political concerns were the primary drivers of political and civil engagement, aspects of Indigenous culture influenced information flows and mobilization among #IdleNoMore tweeters.     

Optimal use of historical information

When historical data are available, incorporating them in an optimal way into the current data analysis can improve the quality of statistical inference. In Bayesian analysis, one can achieve this by using quality-adjusted priors of Zellner, or using power priors of Ibrahim and coauthors. These rules are constructed by raising the prior and/or the sample likelihood to some exponent values, which act as measures of compatibility of their quality or proximity of historical data to current data. This paper presents a general, optimum procedure that unifies these rules and is derived by minimizing a Kullback–Leibler divergence under a divergence constraint. We show that the exponent values are directly related to the divergence constraint set by the user and investigate the effect of this choice theoretically and also through sensitivity analysis. We show that this approach yields ‘100% efficient’ information processing rules in the sense of Zellner. Monte Carlo experiments are conducted to investigate the effect of historical and current sample sizes on the optimum rule. Finally, we illustrate these methods by applying them on real data sets.     

广东与东北都市婚俗调查比较

在社会互动和文化交流中,南北的婚姻文化相互影响,从而使婚俗发生一定程度的变化与趋同。传统陋俗被抛弃。吉祥物和禁忌仍然存在。作为一种文化现象的婚俗,它仍然承担着扩大家族社会关系的功能。婚俗还是原始性与实用性相统一的文化模式。亲友贺婚的风俗一直延续至今。东北和广东的婚俗除了一些细节的差别外,茶和酒分别扮演了不同的角色。南方的婚礼体现了礼尚往来的风俗,而北方的婚礼则有敛财的倾向。     

Efficient Estimation of the Seemingly Unrelated Regression Cointegration Model and Testing for Purchasing Power Parity

This paper studies the efficient estimation of seemingly unrelated linear models with integrated regressors and stationary errors. We consider two cases. The first one has no common regressor among the equations. In this case, we show that by adding leads and lags of the first differences of the regressors and estimating this augmented dynamic regression model by generalized least squares using the long-run covariance matrix, we obtain an efficient estimator of the cointegrating vector that has a limiting mixed normal distribution. In the second case we consider, there is a common regressor to all equations, and we discuss efficient minimum distance estimation in this context. Simulation results suggests that our new estimator compares favorably with others already proposed in the literature. We apply these new estimators to the testing of the proportionality and symmetry conditions implied by purchasing power parity (PPP) among the G-7 countries. The tests based on the efficient estimates easily reject the joint hypotheses of proportionality and symmetry for all countries with either the United States or Germany as numeraire. Based on individual tests, our results suggest that Canada and Germany are the most likely countries for which the proportionality condition holds, and that Italy and Japan for the symmetry condition relative to the United States.     

On linear bilevel problems with multiple objectives at the lower level

Bilevel programming problems provide a framework to deal with decision processes involving two decision makers with a hierarchical structure. They are characterized by the existence of two optimization problems in which the constraint region of the upper level problem is implicitly determined by the lower level optimization problem. This paper focuses on bilevel problems for which the lower level problem is a linear multiobjective program and constraints at both levels define polyhedra. This bilevel problem is reformulated as an optimization problem over a nonconvex region given by a union of faces of the polyhedron defined by all constraints. This reformulation is obtained when dealing with efficient solutions as well as weakly efficient solutions for the lower level problem. Assuming that the upper level objective function is quasiconcave, then an extreme point exists which solves the problem. An exact and a metaheuristic algorithm are developed and their performance is analyzed and compared.     

Estimating the error variance in nonparametric regression by a covariate-matched u-statistic

For nonparametric regression models with fixed and random design, two classes of estimators for the error variance have been introduced: second sample moments based on residuals from a nonparametric fit, and difference-based estimators. The former are asymptotically optimal but require estimating the regression function; the latter are simple but have larger asymptotic variance. For nonparametric regression models with random covariates, we introduce a class of estimators for the error variance that are related to difference-based estimators: covariate-matched U-statistics. We give conditions on the random weights involved that lead to asymptotically optimal estimators of the error variance. Our explicit construction of the weights uses a kernel estimator for the covariate density.     

Scnditignal inference and maximum likelihood in a capture-recapture model

A capture-recapture model is used to illustrate the use of sufficient statistics to factor the joint likelihood function into parts suitable for inferences, valid in samples of any size, on various subsets of the parameters. An assessment is made of the information ignored by confining attention to these parts of the likelihood function. The appropriate application of maximum likelihood estimation is illustrated as providing reasonably accurate approximations to these inferences. Although there are practical examples of this approach in the statistical literature, general awareness of the logical principles involved does not seem widespread. This paper illustrates and explains these logical principles in a more complex situation