A reflected feature space for CART

We present an algorithm for learning oblique decision trees, called HHCART(G). Our decision tree combines learning concepts from two classification trees, HHCART and Geometric Decision Tree (GDT). HHCART(G) is a simplified HHCART algorithm that uses linear structure in the training examples, captured by a modified GDT angle bisector, to define splitting directions. At each node, we reflect the training examples with respect to the modified angle bisector to align this linear structure with the coordinate axes. Searching axis parallel splits in this reflected feature space provides an efficient and effective way of finding oblique splits in the original feature space. Our method is much simpler than HHCART because it only considers one reflected feature space for node splitting. HHCART considers multiple reflected feature spaces for node splitting making it more computationally intensive to build. Experimental results show that HHCART(G) is an effective classifier, producing compact trees with similar or better results than several other decision trees, including GDT and HHCART trees.     

The risks and incentives of disciplinary neoliberal feminism: the case of microfinance

This article develops an analysis of the concept of disciplinary neo-liberal feminism through a focus on gendered poverty alleviation strategies and illustrates the value of this through a discussion of microfinance. By locating this study within an analysis of the expansion of global capital accumulation, the article argues that the liberal frameworks of female empowerment and entrepreneurialism that are central to these programs and to feminism in this form, mask their underlying political, social and economic objectives. In contrast, a Marxist Feminist approach more adequately explains the interplay of class and gender that underpins poverty alleviation strategies. This article argues that in the context of financial crisis and reduced social provision, women living in poverty in the Global South were identified as targets for the expansion of global finance. Their integration into global financial networks via microfinance and other pro-poor strategies has facilitated the expansion of markets for credit while at the same time disciplining market participation through the twin forces of risk and incentive. Disciplinary neo-liberal feminism has underpinned this incorporation of women into global capital accumulation creating profound effects for households and families, with microfinance programs representing important sites of contestation for the politics of class and gender.     

Inference and Decision Making for 21st-Century Drug Development and Approval

ABSTRACT

The cost and time of pharmaceutical drug development continue to grow at rates that many say are unsustainable. These trends have enormous impact on what treatments get to patients, when they get them and how they are used. The statistical framework for supporting decisions in regulated clinical development of new medicines has followed a traditional path of frequentist methodology. Trials using hypothesis tests of “no treatment effect” are done routinely, and the p-value < 0.05 is often the determinant of what constitutes a “successful” trial. Many drugs fail in clinical development, adding to the cost of new medicines, and some evidence points blame at the deficiencies of the frequentist paradigm. An unknown number effective medicines may have been abandoned because trials were declared “unsuccessful” due to a p-value exceeding 0.05. Recently, the Bayesian paradigm has shown utility in the clinical drug development process for its probability-based inference. We argue for a Bayesian approach that employs data from other trials as a “prior” for Phase 3 trials so that synthesized evidence across trials can be utilized to compute probability statements that are valuable for understanding the magnitude of treatment effect. Such a Bayesian paradigm provides a promising framework for improving statistical inference and regulatory decision making.     

Joint models for mixed categorical outcomes: a study of HIV risk perception and disease status in Mozambique

Two types of bivariate models for categorical response variables are introduced to deal with special categories such as ‘unsure’ or ‘unknown’ in combination with other ordinal categories, while taking additional hierarchical data structures into account. The latter is achieved by the use of different covariance structures for a trivariate random effect. The models are applied to data from the INSIDA survey, where interest goes to the effect of covariates on the association between HIV risk perception (quadrinomial with an ‘unknown risk’ category) and HIV infection status (binary). The final model combines continuation-ratio with cumulative link logits for the risk perception, together with partly correlated and partly shared trivariate random effects for the household level. The results indicate that only age has a significant effect on the association between HIV risk perception and infection status. The proposed models may be useful in various fields of application such as social and biomedical sciences, epidemiology and public health.     

“Big Fish in a Small Pond”: Chinese Migrant Shopkeepers in South Africa

The steady growth of Chinese migrants to South Africa in the past decade provides an opportunity to use Sen's (2001, Development as Freedom. Oxford: Oxford University Press) capabilities approach in the field of immigration. This theoretical framing reveals that the Chinese employ, what I call, a small pond migration strategy – utilizing mobility to maximize their social, economic, and human capital. I argue that the Chinese move to South Africa because of a desire to venture out of China and pursue freedoms associated with being one's own boss. Once in South Africa, they choose to stay because of comfortable weather and a slower pace of life, despite losing freedoms associated with high crime in Johannesburg. The findings suggest alternative ways of understanding factors of migration as well as a model that explains migration from more developed countries to less developed ones.     

The influence of FDA advisory information and black box warnings on individual use of prescription antidepressants

We examine how use of antidepressant medications is influenced by Food and Drug Administration (FDA) warnings about the increased risk of suicidality associated with pediatric antidepressant use. With individual-level data on antidepressant use from the Medical Expenditure Panel Survey, we consider whether consumer responses to FDA warnings differ among targeted (children) and non-targeted (adult) age-groups. Because the warning labels specifically mentioned new users, we examine separately the effects of the warnings on initiations of antidepressant therapy and on continued use of antidepressants. We find evidence consistent with reduced initiation of antidepressant use among the intended population of children, and that usage among children with more highly educated parents responded earlier to FDA information. However, we also find spillover effects of reduced initiation among the non-targeted population of adults. Overall, our results indicate that the FDA warning may have led consumers to perceive risks beyond those specifically mentioned.     

Odd-Burr generalized family of distributions with some applications

We study a new family of continuous distributions with two extra shape parameters called the Burr generalized family of distributions. We investigate the shapes of the density and hazard rate function. We derive explicit expressions for some of its mathematical quantities. The estimation of the model parameters is performed by maximum likelihood. We prove the flexibility of the new family by means of applications to two real data sets. Furthermore, we propose a new extended regression model based on the logarithm of the Burr generalized distribution. This model can be very useful to the analysis of real data and provide more realistic fits than other special regression models.     

The generalized odd half-Cauchy family of distributions: Properties and applications

We introduce and study general mathematical properties of a new generator of continuous distributions with one extra parameter called the generalized odd half-Cauchy family. We present some special models and investigate the asymptotics and shapes. The new density function can be expressed as a linear mixture of exponentiated densities based on the same baseline distribution. We derive a power series for the quantile function. We discuss the estimation of the model parameters by maximum likelihood and prove empirically the flexibility of the new family by means of two real data sets.     

A new log-location regression model: estimation,influence diagnostics and residual analysis

We introduce a log-linear regression model based on the odd log-logistic generalized half-normal distribution [7 G.M. Cordeiro, M. Alizadeh, R.R. Pescim, and E.M.M. Ortega, The odd log-logistic generalized half-normal lifetime distribution: Properties and applications, Comm. Statist. Theory Methods (2015), accepted for publication. [Google Scholar]]. Some of its structural properties including explicit expressions for the density function, quantile and generating functions and ordinary moments are derived. We estimate the model parameters by the maximum likelihood method. For different parameter settings, proportion of censoring and sample size, some simulations are performed to investigate the behavior of the estimators. We derive the appropriate matrices for assessing local influence diagnostics on the parameter estimates under different perturbation schemes. We also define the martingale and modified deviance residuals to detect outliers and evaluate the model assumptions. In addition, we demonstrate that the extended regression model can be very useful in the analysis of real data and provide more realistic fits than other special regression models. The potentiality of the new regression model is illustrated by means of a real data set.