Thanks for nothing: Income and labor force participation for never-married mothers since 1982

We examine the changing social and economic characteristics of women who give birth out of wedlock. Using Current Population Survey data collected between 1982 and 2002, we find that never-married mothers remain impoverished. Their income growth over these years was modest despite substantial gains in education, employment, and other individual characteristics generally associated with prosperity. These results affirm the ongoing role of family structure in shaping American inequality.     

Empirical likelihood and Wilks phenomenon for data with nonignorable missing values

Wilks's theorem is useful for constructing confidence regions. When applying the popular empirical likelihood to data with nonignorable nonresponses, Wilks's phenomenon does not hold. This paper unveils that this is caused by the extra estimation of the nuisance parameter in the nonignorable nonresponse propensity. Motivated by this result, we propose an adjusted empirical likelihood for which Wilks's theorem holds. Asymptotic results are presented and supplemented by simulation results for finite sample performance of the point estimators and confidence regions. An analysis of a data set is included for illustration.     

Estimation of total electricity consumption curves by sampling in a finite population when some trajectories are partially unobserved

Millions of smart meters that are able to collect individual load curves, that is, electricity consumption time series, of residential and business customers at fine scale time grids are now deployed by electricity companies all around the world. It may be complex and costly to transmit and exploit such a large quantity of information, therefore it can be relevant to use survey sampling techniques to estimate mean load curves of specific groups of customers. Data collection, like every mass process, may undergo technical problems at every point of the metering and collection chain resulting in missing values. We consider imputation approaches (linear interpolation, kernel smoothing, nearest neighbours, principal analysis by conditional estimation) that take advantage of the specificities of the data, that is to say the strong relation between the consumption at different instants of time. The performances of these techniques are compared on a real example of Irish electricity load curves under various scenarios of missing data. A general variance approximation of total estimators is also given which encompasses nearest neighbours, kernel smoothers imputation and linear imputation methods. The Canadian Journal of Statistics 47: 65–89; 2019 © 2018 Statistical Society of Canada     

新生代农民工权益保障存在的问题及对策

新生代农民工是城市建设大军的重要组成部分,但他们在融入城市的过程中各种合法权益不能得到有效保障。文章在客观分析新生代农民工权益受侵原因的基础上,提出了维护新生代农民工权益的系列对策。     

The Role of Pre‐ and Postconception Relationships for First‐Time Parents

Using data from the National Longitudinal Survey of Youth 1997, a nationally representative cohort of young adults, the authors analyzed relationship type at the time of a first birth (N = 4,044). More than 10% of births were to a postconception cohabiting household (cohabitations that were initiated between conception and birth), a higher proportion of births than those born to postconception married households. Individuals in postconception and preconception cohabiting relationships (cohabitations that existed prior to conception) were demographically similar; both groups were associated with lower levels of socioeconomic advantage relative to those in preconception and postconception marriage. Postconception and preconception cohabiting relationships were associated with similar levels of dissolution, as 40% dissolved within 3 years of a child's birth. Having a marital union, rather than whether relationship was established pre‐ or postconception, was more strongly associated with who selected into the relationship and how long the relationship lasted.     

Handling incomplete Quality-of-Life Data

Incomplete data sets are often encountered in theanalysis of quality-of-life (QOL) data. The incompleteness arisesfrom two major sources, namely, missing responses and artificialquantification of response categories. Shen and Lai (1998a)propose using Optimal Scaling (OS) to tackle the problem. The OSmethod based on numerical iterative approach attempts to restorethe continuous property of the measurements and provide estimatesfor missing responses. However, the OS leads to convergenceproblem when there are many missing values in the data set; andit incorporates no mechanisms to provide the standard errors ofthe mean estimates when missing values are filled. Hot-deckimputation is therefore suggested. This paper presents asimulation study to show that the random hot-deck imputationyields reasonable estimates for the population mean and generallypreserves the distribution of the population. In addition, whenapplying the random hot-deck imputation, valid estimates for thestandard error of the mean estimate can be obtained using thevariance formula due to Lai (1998). With hot-deck imputationtaking care of the missing responses and OS quantifying theresponse categories, it is postulated that the problem of dataincompleteness can be more satisfactorily handled. By applyingthe proposed techniques to real survey data, this paper alsopresents the change of the QOL of Hong Kong residents in the lastdecade leading to the turning point of the metropolis in 1997.     

Ensemble Approaches to Estimating the Population Mean with Missing Response

We propose new ensemble approaches to estimate the population mean for missing response data with fully observed auxiliary variables. We first compress the working models according to their categories through a weighted average, where the weights are proportional to the square of the least‐squares coefficients of model refitting. Based on the compressed values, we develop two ensemble frameworks, under which one is to adjust weights in the inverse probability weighting procedure and the other is built upon an additive structure by reformulating the augmented inverse probability weighting function. The asymptotic normality property is established for the proposed estimators through the theory of estimating functions with plugged‐in nuisance parameter estimates. Simulation studies show that the new proposals have substantial advantages over existing ones for small sample sizes, and an acquired immune deficiency syndrome data example is used for illustration.     

数据随机缺失下分位数回归模型的诱导光滑估计法

在数据随机缺失的分位数回归模型中,运用诱导光滑思想构造光滑的估计方程,得到了回归参数的诱导光滑估计及渐近协方差估计。接着证明了诱导光滑估计的渐近正态性质,并给出诱导光滑估计及其渐近协方差估计的算法。模拟研究表明新方法在有限样本中表现出色。     

Dimension-reduced empirical likelihood inference for response mean with data missing at random

To make efficient inference for mean of a response variable when the data are missing at random and the dimension of covariate is not low, we construct three bias-corrected empirical likelihood (EL) methods in conjunction with dimension-reduced kernel estimation of propensity or/and conditional mean response function. Consistency and asymptotic normality of the maximum dimension-reduced EL estimators are established. We further study the asymptotic properties of the resulting dimension-reduced EL ratio functions and the corresponding EL confidence intervals for the response mean are constructed. The finite-sample performance of the proposed estimators is studied through simulation, and an application to HIV-CD4 data set is also presented.