‘Lady,Leave My Culture Out of It!’ An Interview With Nada Miocevic

Nada Miocevic is a social worker and family therapist who trained at Zagreb University in Croatia and at Melbourne University in Victoria, Australia. She completed her training in family therapy in 1975 at the Bouverie Centre, Melbourne. Since migrating to Australia in 1967, her work with migrant and refugee families has taken her throughout Australia and overseas. Currently she is in private practice. Her work involves conducting training courses in supervision and supervision of supervision, as well as her continuing work with families who experience long‐term illnesses.     

Measuring the economic impacts of recent oil price shocks on oil-dependent economy: evidence from Malaysia

This paper examines the economy-wide impacts of recent oil price shocks on the Malaysian economy. To achieve this objective, an integrated methodological framework that combines econometric and input–output models is utilized to assess the impacts of an oil price shock on tax revenues, employment, labor income and gross domestic product (GDP). Our results reveal that the recent oil price shocks significantly affects these macroeconomic variables. The decline in oil prices from 2015 to 2016 reduces tax revenues by 10.5%, lower GDP by 1.9% and increases the unemployment rate by 0.3%. As such, the sharp crunch in oil prices serves as a reminder to policymakers on the vulnerability inherent in overreliance on oil exports and the urgent need to diversify the economy.     

Effect of financial development on economic growth in sub-Saharan Africa

This paper examines the overall economic growth effect when the growth in finance and real sector is disproportionate relying on panel data for 29 sub-Saharan African countries over the period 1980–2014. Results from the system generalized methods of moments (GMM) reveal that, while financial development supports economic growth, the extent to which finance helps growth depends crucially on the simultaneous growth of real and financial sectors. The elasticity of growth to changes in either size of the real or financial sector is higher under balanced sectoral growth. We also show that rapid and unbridled credit growth comes at a huge cost to economic growth with consequences stemming from financing of risky and unsustainable investments coupled with superfluous consumption fueling inflation. However, the pass-through excess finance–economic growth effect via the investment channel is stronger.     

Multiscale Adaptive Regression Models for Neuroimaging Data

Neuroimaging studies aim to analyze imaging data with complex spatial patterns in a large number of locations (called voxels) on a two-dimensional (2D) surface or in a 3D volume. Conventional analyses of imaging data include two sequential steps: spatially smoothing imaging data and then independently fitting a statistical model at each voxel. However, conventional analyses suffer from the same amount of smoothing throughout the whole image, the arbitrary choice of smoothing extent, and low statistical power in detecting spatial patterns. We propose a multiscale adaptive regression model (MARM) to integrate the propagation-separation (PS) approach (Polzehl and Spokoiny, 2000, 2006) with statistical modeling at each voxel for spatial and adaptive analysis of neuroimaging data from multiple subjects. MARM has three features: being spatial, being hierarchical, and being adaptive. We use a multiscale adaptive estimation and testing procedure (MAET) to utilize imaging observations from the neighboring voxels of the current voxel to adaptively calculate parameter estimates and test statistics. Theoretically, we establish consistency and asymptotic normality of the adaptive parameter estimates and the asymptotic distribution of the adaptive test statistics. Our simulation studies and real data analysis confirm that MARM significantly outperforms conventional analyses of imaging data.     

A generalized linear mixed model for longitudinal binary data with a marginal logit link function

Longitudinal studies of a binary outcome are common in the health, social, and behavioral sciences. In general, a feature of random effects logistic regression models for longitudinal binary data is that the marginal functional form, when integrated over the distribution of the random effects, is no longer of logistic form. Recently, Wang and Louis (2003) proposed a random intercept model in the clustered binary data setting where the marginal model has a logistic form. An acknowledged limitation of their model is that it allows only a single random effect that varies from cluster to cluster. In this paper, we propose a modification of their model to handle longitudinal data, allowing separate, but correlated, random intercepts at each measurement occasion. The proposed model allows for a flexible correlation structure among the random intercepts, where the correlations can be interpreted in terms of Kendall's τ. For example, the marginal correlations among the repeated binary outcomes can decline with increasing time separation, while the model retains the property of having matching conditional and marginal logit link functions. Finally, the proposed method is used to analyze data from a longitudinal study designed to monitor cardiac abnormalities in children born to HIV-infected women.     

Estimating the population mean in the case of missing data using simple random sampling

In this paper, we suggest three new ratio estimators of the population mean using quartiles of the auxiliary variable when there are missing data from the sample units. The suggested estimators are investigated under the simple random sampling method. We obtain the mean square errors equations for these estimators. The suggested estimators are compared with the sample mean and ratio estimators in the case of missing data. Also, they are compared with estimators in Singh and Horn [Compromised imputation in survey sampling, Metrika 51 (2000), pp. 267–276], Singh and Deo [Imputation by power transformation, Statist. Papers 45 (2003), pp. 555–579], and Kadilar and Cingi [Estimators for the population mean in the case of missing data, Commun. Stat.-Theory Methods, 37 (2008), pp. 2226–2236] and present under which conditions the proposed estimators are more efficient than other estimators. In terms of accuracy and of the coverage of the bootstrap confidence intervals, the suggested estimators performed better than other estimators.     

Joint generalized estimating equations for multivariate longitudinal binary outcomes with missing data: an application to acquired immune deficiency syndrome data

Summary.  In a large, prospective longitudinal study designed to monitor cardiac abnormalities in children born to women who are infected with the human immunodeficiency virus, instead of a single outcome variable, there are multiple binary outcomes (e.g. abnormal heart rate, abnormal blood pressure and abnormal heart wall thickness) considered as joint measures of heart function over time. In the presence of missing responses at some time points, longitudinal marginal models for these multiple outcomes can be estimated by using generalized estimating equations (GEEs), and consistent estimates can be obtained under the assumption of a missingness completely at random mechanism. When the missing data mechanism is missingness at random, i.e. the probability of missing a particular outcome at a time point depends on observed values of that outcome and the remaining outcomes at other time points, we propose joint estimation of the marginal models by using a single modified GEE based on an EM-type algorithm. The method proposed is motivated by the longitudinal study of cardiac abnormalities in children who were born to women infected with the human immunodeficiency virus, and analyses of these data are presented to illustrate the application of the method. Further, in an asymptotic study of bias, we show that, under a missingness at random mechanism in which missingness depends on all observed outcome variables, our joint estimation via the modified GEE produces almost unbiased estimates, provided that the correlation model has been correctly specified, whereas estimates from standard GEEs can lead to substantial bias.