The frequency of sexual abuse and depression in a sample of street children of one of deprived districts of Tehran

The purpose of this study was to determine the frequency of sexual abuse and depression among street children who live in a deprived district of Tehran. The researchers used the K-SADS questionnaire and a clinical interview were used to evaluate depression and sexual abuse in 87 street children in Tehran. Eighteen (20.9%) of the children had been sexually abused. Depressed children were 3.2 times more likely to be sexually abused than non-depressed children. Furthermore, 26 girls (86.7%) and 27 boys (48.2%) suffered from depression. The frequency of depression demonstrated a significant association with the father's or breadwinner's history of imprisonment or unemployment. Interventional programs providing education and support should be implemented for street children.     

Optimal estimation in functional linear regression for sparse noise‐contaminated data

In this article, we propose a novel approach to fit a functional linear regression in which both the response and the predictor are functions. We consider the case where the response and the predictor processes are both sparsely sampled at random time points and are contaminated with random errors. In addition, the random times are allowed to be different for the measurements of the predictor and the response functions. The aforementioned situation often occurs in longitudinal data settings. To estimate the covariance and the cross‐covariance functions, we use a regularization method over a reproducing kernel Hilbert space. The estimate of the cross‐covariance function is used to obtain estimates of the regression coefficient function and of the functional singular components. We derive the convergence rates of the proposed cross‐covariance, the regression coefficient, and the singular component function estimators. Furthermore, we show that, under some regularity conditions, the estimator of the coefficient function has a minimax optimal rate. We conduct a simulation study and demonstrate merits of the proposed method by comparing it to some other existing methods in the literature. We illustrate the method by an example of an application to a real‐world air quality dataset. The Canadian Journal of Statistics 47: 524–559; 2019 © 2019 Statistical Society of Canada     

On the ratio of two independent skewnesses

In many fields, the researchers are interested in making inferences about the ratio of skewnesses in two independent populations. In the present paper, the asymptotic distribution for the ratio of the sample skewnesses in two independent populations is established. Then the asymptotic distribution is used to derive the asymptotic confidence interval and to test the hypothesis for the ratio of population's skewnesses. Finally, the applicability of the proposed method is investigated through Monte Carlo simulations.     

Local Box–Cox transformation on time-varying parametric models for smoothing estimation of conditional CDF with longitudinal data

Nonparametric estimation and inferences of conditional distribution functions with longitudinal data have important applications in biomedical studies, such as epidemiological studies and longitudinal clinical trials. Estimation approaches without any structural assumptions may lead to inadequate and numerically unstable estimators in practice. We propose in this paper a nonparametric approach based on time-varying parametric models for estimating the conditional distribution functions with a longitudinal sample. Our model assumes that the conditional distribution of the outcome variable at each given time point can be approximated by a parametric model after local Box–Cox transformation. Our estimation is based on a two-step smoothing method, in which we first obtain the raw estimators of the conditional distribution functions at a set of disjoint time points, and then compute the final estimators at any time by smoothing the raw estimators. Applications of our two-step estimation method have been demonstrated through a large epidemiological study of childhood growth and blood pressure. Finite sample properties of our procedures are investigated through a simulation study. Application and simulation results show that smoothing estimation from time-variant parametric models outperforms the existing kernel smoothing estimator by producing narrower pointwise bootstrap confidence band and smaller root mean squared error.     

Evaluating the potential contribution of urban ecosystem service to climate change mitigation

Urban Ecosystems - Promoting urban greenery through tree planting strategies has been considered as a measure to mitigate climate change. While it is essential to understand the temporal dynamics...     

‘Mind the Gap’: An Analysis of Gender Wage Differentials in Russia

This paper provides new estimates for male-female earnings differentials in Russia, incorporating the use of the Heckman (Econometrica 47: 153–161, 1979) two-step procedure for sample selection bias. This is a necessary adjustment in the case of female earnings because women who participate in the labour market may be a non-random sub-set of those who could work. This is a technique that enables the participation decision of women to be modelled and their earnings corrected for self-selection. The gender gap is then calculated using Oaxaca (International Economic Review 14: 693–709, 1973) and Reimers’ (Review of Economics and Statistics 65: 570–579, 1983) methods. The results indicate that the unexplained part of the earnings differential is smaller than in other studies that did not correct for sample selection.     

Spatial analysis of auto-multivariate lattice data

Most problems related to environmental studies are innately multivariate. In fact, in each spatial location more than one variable is usually measured. In geostatistics multivariate data analysis, where we intend to predict the value of a random vector in a new site, which has no data, cokriging method is used as the best linear unbiased prediction. In lattice data analysis, where almost exclusively the probability modeling of data is of concern, only auto-Gaussian model has been used for continuous multivariate data. For discrete multivariate data little work has been carried out. In this paper, an auto-multinomial model is suggested for analyzing multivariate lattice discrete data. The proposed method is illustrated by a real example of air pollution in Tehran, Iran.     

Divorce and the cost of housing: evidence from Iran

Divorce trend in Iran has become a serious social concern that is suspected of being influenced by rising housing costs in an oil-based economy. Iran has the highest growth rate of divorce among Islamic countries in the Middle East and North Africa region. Using data from 30 provinces of Iran from 2002 to 2010, this paper examines the relationship between housing costs (house prices and rents) and divorce rate, controlling for other macroeconomic variables such as unemployment, inflation, and education in addition to regional, cultural, traditional, and conventional attitudes toward divorce. By applying panel fixed-effects and dynamic generalized methods of moments methods, our results suggest that increases in housing costs erode marital stability in Iran. Our main results are also supported when we focus on the shocks in housing costs, using the Vector autoregressive based impulse response and variance decomposition analyses of divorce rates at the national level from 1982 to 2010.     

The Clustering of Psychiatric Disorders in High-Risk Gambling Populations

The aims of this study were to examine the associations between psychiatric disorders and pathological gambling (PG) and the clustering of psychiatric disorders in high risk gambler populations. The sample comprised 140 regular gamblers who were recruited from the general public. A variety of self- report and semi structured questionnaires was administered, including the Mini International Neuropsychiatric Interview, The Personality Diagnostic Questionnaire-4th Edition, NORC DSM-IV Screen for Gambling Problems Self- administered and Problem Gambling Severity Index. Axis I and Axis II psychiatric disorders and personality disturbances were found to be more prevalent amongst pathological gamblers than other gamblers with the strongest differences observed for mood and anxiety-related disorders. Almost two-thirds of pathological gamblers reported both an anxiety or mood disorder in conjunction with another type of disorder. These differences between the gambling groups existed even after controlling for gender. The results highlight the high rates of co-morbidity in pathological gamblers in the community and the extent to which anxiety and mood disorders co-existing with other forms of pathology. These results highlight the significant challenges facing treatment services in the treatment of PG and the extent to which this should be treated as the primary disorder.