Detecting Directionality in Time Series

Abstract

Directionality can be seen in many stationary time series from various disciplines, but it is overlooked when fitting linear models with Gaussian errors. Moreover, we cannot rely on distinguishing directionality by comparing a plot of a time series in time order with a plot in reverse time order. In general, a statistical measure is required to detect and quantify directionality. There are several quite different qualitative forms of directionality, and we distinguish: rapid rises followed by slow recessions; rapid increases and rapid decreases from the mean followed by slow recovery toward the mean; directionality above or below some threshold; and intermittent directionality. The first objective is to develop a suite of statistical measures that will detect directionality and help classify its nature. The second objective is to demonstrate the potential benefits of detecting directionality. We consider applications from business, environmental science, finance, and medicine. Time series data are collected from many processes, both natural and anthropogenic, by a wide range of organizations, and directionality can easily be monitored as part of routine analysis. We suggest that doing so may provide new insights to the processes.     

Public sector delivery of waste management services: cases from the Indian Sub-Continent

This paper discusses the role of public sector institutions in the delivery of urban services, with a particular focus on waste management in the Indian Sub-Continent. Public sector institutions are the major stakeholders in the delivery of urban sevices. They hold the major responsibilities, funds and employ a large number of staff. The public sector institutions are also exposed to political influence and the institutional changes are often politically or donors driven. However, since new institutions such as non-government organisations and private sector are taking important roles in the actual delivery of services, there is a greater need to understand the existing linkages to promote integration. The informal sector also provides services to a large number of population. In order to promote efficient and effective delivery of urban services, it is important to work on the integrated approaches involving key stakeholders.     

Varieties of Ignorance

This paper considers three types and levels of ignorance about the professions: expert, amateur, and professional. The empirical data comprise my own book about the professions, the Wikipedia article on that subject, and the 105 papers that cited my book in 2008. In these three I separate ignorance of facts, of literatures, and of skills, characterizing each type of ignorance by its mix of the three. Amateur ignorance is mostly of skills, and professional ignorance mostly ignorance of collateral literatures. Expert ignorance reflects the use of theory as a mnemonic device and so is particularly insidious.     

More than just nickels and dimes: a cross-national analysis of working poverty in affluent democracies

Despite its centrality to contemporary inequality, working poverty is often popularly discussed but rarely studied by sociologists. Using the Luxembourg Income Study (2009), we analyze whether an individual is working poor across 18 affluent democracies circa 2000. We demonstrate that working poverty does not simply mirror overall poverty and that there is greater cross-national variation in working than overall poverty. We then examine four explanations for working poverty: demographic characteristics, economic performance, unified theory, and welfare generosity. We utilize Heckman probit models to jointly model the likelihood of employment and poverty among the employed. Our analyses provide the least support for the economic performance explanation. There is modest support for unified theory as unionization reduces working poverty in some models. However, most of these effects appear to be mediated by welfare generosity. More substantial evidence exists for the demographic characteristics and welfare generosity explanations. An individual's likelihood of being working poor can be explained by (a) a lack of multiple earners or other adults in one's household, low education, single motherhood, having children and youth; and (b) the generosity of the welfare state in which he or she resides. Also, welfare generosity does not undermine employment and reduces working poverty even among demographically vulnerable groups. Ultimately, we encourage a greater role for the welfare state in debates about working poverty.     

Discrimination Hurts: The Academic,Psychological, and Physical Well‐Being of Adolescents

Little is known about the frequency of ethnic or racial discrimination and its implications for Latin American and Asian youths' development. In this study, we examined if there were ethnic and generation differences among 601 12th graders from Latin American (36%), Asian (43%), and European (19%) backgrounds in the frequency of peer, adult, and daily discrimination, and whether discrimination predicted their well‐being. Adolescents from Latin American and Asian backgrounds reported more adult and peer discrimination than their peers from European backgrounds. Latin American youth reported more adult discrimination than their Asian peers. Discrimination predicted lower grade point averages and self‐esteem, and more depressive symptoms, distress, and physical complaints. Ethnic identity, ethnic socialization, and race rejection sensitivity did not moderate the associations between discrimination and well‐being.     

Model selection using information criteria under a new estimation method: least squares ratio

In this study, we evaluate several forms of both Akaike-type and Information Complexity (ICOMP)-type information criteria, in the context of selecting an optimal subset least squares ratio (LSR) regression model. Our simulation studies are designed to mimic many characteristics present in real data – heavy tails, multicollinearity, redundant variables, and completely unnecessary variables. Our findings are that LSR in conjunction with one of the ICOMP criteria is very good at selecting the true model. Finally, we apply these methods to the familiar body fat data set.     

Bayesian parametric accelerated failure time spatial model and its application to prostate cancer

Prostate cancer (PrCA) is the most common cancer diagnosed in American men and the second leading cause of death from malignancies. There are large geographical variation and racial disparities existing in the survival rate of PrCA. Much work on the spatial survival model is based on the proportional hazards (PH) model, but few focused on the accelerated failure time (AFT) model. In this paper, we investigate the PrCA data of Louisiana from the Surveillance, Epidemiology, and End Results program and the violation of the PH assumption suggests that the spatial survival model based on the AFT model is more appropriate for this data set. To account for the possible extra-variation, we consider spatially referenced independent or dependent spatial structures. The deviance information criterion is used to select a best-fitting model within the Bayesian frame work. The results from our study indicate that age, race, stage, and geographical distribution are significant in evaluating PrCA survival.