How Many More Missing Women? Excess Female Mortality and Prenatal Sex Selection, 1970–2050

Sex‐based discrimination has resulted in severe demographic imbalances between males and females, culminating in a large number of “missing women” in several countries around the world. We provide new estimates and projections of the number of missing females and of the roles played by prenatal and postnatal factors in this imbalance. We estimate time series of the number of missing females, the number of excess female deaths, and the number of missing female births for the world and selected countries. Estimates are provided for 1970–2010 and projections are made from 2010 to 2050. We show that the estimates of these different indicators are consistent with one another and account for the dynamics of the population of missing females over time. We conclude that the number of missing females has steadily risen in the past decades, reaching 126 million in 2010, and the number is expected to peak at 150 million in 2035. Excess mortality was the dominant cause of missing females in the past, and this is expected to remain the case in future decades in spite of the recent rise of prenatal sex selection. The annual number of newly missing females reached 3.4 million in 2010 and is expected to remain above 3 million every year until 2050.     

Confidence Corridors for Multivariate Generalized Quantile Regression

We focus on the construction of confidence corridors for multivariate nonparametric generalized quantile regression functions. This construction is based on asymptotic results for the maximal deviation between a suitable nonparametric estimator and the true function of interest, which follow after a series of approximation steps including a Bahadur representation, a new strong approximation theorem, and exponential tail inequalities for Gaussian random fields. As a byproduct we also obtain multivariate confidence corridors for the regression function in the classical mean regression. To deal with the problem of slowly decreasing error in coverage probability of the asymptotic confidence corridors, which results in meager coverage for small sample sizes, a simple bootstrap procedure is designed based on the leading term of the Bahadur representation. The finite-sample properties of both procedures are investigated by means of a simulation study and it is demonstrated that the bootstrap procedure considerably outperforms the asymptotic bands in terms of coverage accuracy. Finally, the bootstrap confidence corridors are used to study the efficacy of the National Supported Work Demonstration, which is a randomized employment enhancement program launched in the 1970s. This article has supplementary materials online.     

Family engagement within the context of assessment in child protection practice: The case of Estonia

Engagement in the context of child protection is of significant international interest, as the participation of family is a central notion in child and family social work. Drawing on in‐depth, semistructured interviews with a sample of 26 child protection workers in 1 region in Estonia, this article reports the findings of the participants' perspectives on family engagement in the context of child protection assessment. Child protection workers perceive the engagement related to 3 aspects: first, it is associated to the acts of the worker; second, it is associated to parental part of actions—depending on parents themselves; and third, engagement was seen as “doing together” with the family, referring to partnership. The aim of the engagement was mainly considered in the terms of empowerment—empowering family to cope on their own and to take charge of their lives. The negative image of child protection was recognised as one of the main barriers to the family engagement. Understanding the family and their situation was seen as the basis for a trusting relationship, being the most crucial factor to promoting family engagement.     

Local influence in seemingly unrelated regression model with ridge estimate

Local influence is a well-known method for identifying the influential observations in a dataset and commonly needed in a statistical analysis. In this paper, we study the local influence on the parameters of interest in the seemingly unrelated regression model with ridge estimation, when there exists collinearity among the explanatory variables. We examine two types of perturbation schemes to identify influential observations: the perturbation of variance and the perturbation of individual explanatory variables. Finally, the efficacy of our proposed method is illustrated by analyzing [13 A. Munnell, Why has productivity declined? Productivity and public investment, New Engl. Econ. Rev. (1990), pp. 3–22. [Google Scholar]] productivity dataset.     

A new nonlinear asymmetric cointegration approach using error correction models

In this article, two new powerful tests for cointegration are proposed. The general idea is based on an intuitively appealing extension of the traditional, rather restrictive cointegration concept. In this article, we allow for a nonlinear, but most importantly a different, asymmetric convergence process to account for negative and positive changes in our cointegration approach. Using Monte Carlo simulations we verify, that the estimated size of the first test depends on the unknown value of a signal-to-noise ratio q. However, our second test—which is based on the original ideas of Kanioura and Turner—is more successful and robust in the sense that it works in all of the different evaluated situations. Furthermore it is shown to be more powerful than the traditional residual based Enders and Siklos method. The new optimal test is also applied in an empirical example in order to test for potential nonlinear asymmetric price transmission effects on the Swedish power market. We find that there is a higher propensity for power retailers to rapidly and systematically increase their retail electricity prices subsequent to increases in Nordpool's wholesale prices, than there is for them to reduce their prices subsequent to a drop in wholesale spot prices.     

A new family of multivariate slash distributions

In this article, a new form of multivariate slash distribution is introduced and some statistical properties are derived. In order to illustrate the advantage of this distribution over the existing generalized multivariate slash distribution in the literature, it is applied to a real data set.     

Estimation of reliability of multicomponent stress–strength for a Kumaraswamy distribution

This article deals with the Bayesian and non Bayesian estimation of multicomponent stress–strength reliability by assuming the Kumaraswamy distribution. Both stress and strength are assumed to have a Kumaraswamy distribution with common and known shape parameter. The reliability of such a system is obtained by the methods of maximum likelihood and Bayesian approach and the results are compared using Markov Chain Monte Carlo (MCMC) technique for both small and large samples. Finally, two data sets are analyzed for illustrative purposes.     

VIF-based adaptive matrix perturbation method for heteroskedasticity-robust covariance estimators in the presence of multicollinearity

In this study, we investigate linear regression having both heteroskedasticity and collinearity problems. We discuss the properties related to the perturbation method. Important observations are summarized as theorems. We then prove the main result that states the heteroskedasticity-robust variances can be improved and that the resulting bias is minimized by using the matrix perturbation method. We analyze a practical example for validation of the method.