Child labour measurement: Whom should we ask?

Funding decisions to support vulnerable children are tied to child labour statistics, hence the importance of an accurate measurement. The author analyses whether the type of respondent plays an important role in explaining variations in child labour statistics. Using data from two sites in Tanzania, the analysis shows that “whom we ask” matters considerably when estimating the prevalence of child labour. The results suggest that prevalence increases by approximately 35 to 65 per cent when using child self‐reports rather than proxy reports. This bias affects 14 to 31 per cent of the sample, depending on the indicator. Discrepancies decrease as the child ages and increase if proxy attitudes demonstrate opposition to child labour.     

Legal Marriage,Unequal Recognition,and Mental Health Among Same‐Sex Couples

The authors examined whether the perception of unequal relationship recognition ‐ a novel couple‐level minority stressor ‐ has negative consequences for mental health among same‐sex couples. Data were analyzed from a dyadic study of 100 same‐sex couples (200 individuals) in the United States. Being in a legal marriage was associated with lower perceived unequal recognition and better mental health; being in a registered domestic partnership or civil union—but not also legally married—was associated with greater perceived unequal recognition and worse mental health. Actor partner interdependence models tested associations between legal relationship status, unequal relationship recognition, and mental health (nonspecific psychological distress, depressive symptomatology, and problematic drinking), net controls (age, gender, race and ethnicity, education, and income). Unequal recognition was consistently associated with worse mental health, independent of legal relationship status. Legal changes affecting relationship recognition should not be seen as simple remedies for addressing the mental health effects of institutionalized discrimination.     

Integrated methodology for multiple systems estimation and record linkage using a missing data formulation

There are now three essentially separate literatures on the topics of multiple systems estimation, record linkage, and missing data. But in practice the three are intimately intertwined. For example, record linkage involving multiple data sources for human populations is often carried out with the expressed goal of developing a merged database for multiple system estimation (MSE). Similarly, one way to view both the record linkage and MSE problems is as ones involving the estimation of missing data. This presentation highlights the technical nature of these interrelationships and provides a preliminary effort at their integration.     

制造业效率区域差异及其影响因素分析——基于中国省级面板数据的研究

制造业的发展关系到第二产业乃至整个国民经济的发展,制造业的增长是中国工业经济增长的主导力量。本文运用数据包络分析法,测度了制造业各行业及中国各省制造业效率情况,然后利用中国省级制造业效率指标以及专利申请、地区经济发展水平等制造业效率影响因素的外延数据构成面板数据（Panel Data）建立模型。通过实证分析得出：地区经济的发展有利于制造业效率的提升。专利申请与制造业效率呈负相关。     

SEMIPARAMETRIC REGRESSION AND GRAPHICAL MODELS

Semiparametric regression models that use spline basis functions with penalization have graphical model representations. This link is more powerful than previously established mixed model representations of semiparametric regression, as a larger class of models can be accommodated. Complications such as missingness and measurement error are more naturally handled within the graphical model architecture. Directed acyclic graphs, also known as Bayesian networks, play a prominent role. Graphical model-based Bayesian 'inference engines', such as bugs and vibes , facilitate fitting and inference. Underlying these are Markov chain Monte Carlo schemes and recent developments in variational approximation theory and methodology.     

Deconvolution methods for non-parametric inference in two-level mixed models

Summary.  We develop a general non-parametric approach to the analysis of clustered data via random effects. Assuming only that the link function is known, the regression functions and the distributions of both cluster means and observation errors are treated non-parametrically. Our argument proceeds by viewing the observation error at the cluster mean level as though it were a measurement error in an errors-in-variables problem, and using a deconvolution argument to access the distribution of the cluster mean. A Fourier deconvolution approach could be used if the distribution of the error-in-variables were known. In practice it is unknown, of course, but it can be estimated from repeated measurements, and in this way deconvolution can be achieved in an approximate sense. This argument might be interpreted as implying that large numbers of replicates are necessary for each cluster mean distribution, but that is not so; we avoid this requirement by incorporating statistical smoothing over values of nearby explanatory variables. Empirical rules are developed for the choice of smoothing parameter. Numerical simulations, and an application to real data, demonstrate small sample performance for this package of methodology. We also develop theory establishing statistical consistency.     

The generalized log-gamma mixture model with covariates: local influence and residual analysis

In a sample of censored survival times, the presence of an immune proportion of individuals who are not subject to death, failure or relapse, may be indicated by a relatively high number of individuals with large censored survival times. In this paper the generalized log-gamma model is modified for the possibility that long-term survivors may be present in the data. The model attempts to separately estimate the effects of covariates on the surviving fraction, that is, the proportion of the population for which the event never occurs. The logistic function is used for the regression model of the surviving fraction. Inference for the model parameters is considered via maximum likelihood. Some influence methods, such as the local influence and total local influence of an individual are derived, analyzed and discussed. Finally, a data set from the medical area is analyzed under the log-gamma generalized mixture model. A residual analysis is performed in order to select an appropriate model. The authors would like to thank the editor and referees for their helpful comments. This work was supported by CNPq, Brazil.     

Robust nonparametric estimation with missing data

In this paper, under a nonparametric regression model, we introduce two families of robust procedures to estimate the regression function when missing data occur in the response. The first proposal is based on a local M$M$-functional applied to the conditional distribution function estimate adapted to the presence of missing data. The second proposal imputes the missing responses using the local M$M$-smoother based on the observed sample and then estimates the regression function with the completed sample. We show that the robust procedures considered are consistent and asymptotically normally distributed. A robust procedure to select the smoothing parameter is also discussed.     

Finite normal mixture copulas for multivariate discrete data modeling

A new family of copulas is introduced that provides flexible dependence structure while being tractable and simple to use for multivariate discrete data modeling. The construction exploits finite mixtures of uncorrelated normal distributions. Accordingly, the cumulative distribution function is simply the product of univariate normal distributions. At the same time, however, the mixing operation introduces association. The properties of the new family of copulas are examined and a concrete application is used to show its applicability.     

Copula-based regression models: A survey

In this review paper we collect several results about copula-based models, especially concerning regression models, by focusing on some insurance applications.