BIG DATA AND BIG CITIES: THE PROMISES AND LIMITATIONS OF IMPROVED MEASURES OF URBAN LIFE

New, “big data” sources allow measurement of city characteristics and outcome variables at higher collection frequencies and more granular geographic scales than ever before. However, big data will not solve large urban social science questions on its own. Big urban data has the most value for the study of cities when it allows measurement of the previously opaque, or when it can be coupled with exogenous shocks to people or place. We describe a number of new urban data sources and illustrate how they can be used to improve the study and function of cities. We first show how Google Street View images can be used to predict income in New York City, suggesting that similar imagery data can be used to map wealth and poverty in previously unmeasured areas of the developing world. We then discuss how survey techniques can be improved to better measure willingness to pay for urban amenities. Finally, we explain how Internet data is being used to improve the quality of city services. (JEL R1, C8, C18)     

Partial least squares estimator for single-index models

The partial least squares (PLS) approach first constructs new explanatory variables, known as factors (or components), which are linear combinations of available predictor variables. A small subset of these factors is then chosen and retained for prediction. We study the performance of PLS in estimating single-index models, especially when the predictor variables exhibit high collinearity. We show that PLS estimates are consistent up to a constant of proportionality. We present three simulation studies that compare the performance of PLS in estimating single-index models with that of sliced inverse regression (SIR). In the first two studies, we find that PLS performs better than SIR when collinearity exists. In the third study, we learn that PLS performs well even when there are multiple dependent variables, the link function is non-linear and the shape of the functional form is not known.     

A comparative study on detection of influential observations in linear regression

A large number of statistics are used in the literature to detect outliers and influential observations in the linear regression model. In this paper comparison studies have been made for determining a statistic which performs better than the other. This includes: (i) a detailed simulation study, and (ii) analyses of several data sets studied by different authors. Different choices of the design matrix of regression model are considered. Design A studies the performance of the various statistics for detecting the scale shift type outliers, and designs B and C provide information on the performance of the statistics for identifying the influential observations. We have used cutoff points using the exact distributions and Bonferroni's inequality for each statistic. The results show that the studentized residual which is used for detection of mean shift outliers is appropriate for detection of scale shift outliers also, and the Welsch's statistic and the Cook's distance are appropriate for detection of influential observations.     

Severe self-neglect: an epidemiological and historical perspective

Older adults with severe self-neglect have multiple deficits in various social, functional and physical domains, and often live insqualor. These individuals often present with poor personal hygiene, domestic squalor and hoarding which results in a threat to their own health as well as personal and public safety. Severe self-neglect occurs along a continuum with older adults often having cognitive and affective disorders compared with younger individuals presenting with psychiatric illnesses. In cases of severe self-neglect with hoarding, evidence has shown this behavior occurs in diverse social strata and not among the wealthy and professionals alone as believed earlier. Due to the multiple conditions associated with severe self-neglect, this population will require an interdisciplinary, multidimensional approach to reduce morbidity and mortality rates including nursing home placement. Research on this group has been limited and is rarely described in the medical and nursing literature. Future research is needed to provide practitioners with effective screening instruments and interventions on cases of severe self-neglect.     

On detection of outliers in symmetric normal models

Extensions of recent results for detection of mean slippage type outliers from i.i.d. multivariate normal and elliptically symmetric distributions are made to symmetric case, that is, when the observations are equicorrelated. The main tool used is Wijsman's (1967) representation theorem. The results obtained can be viewed as a robustness property of the use of Mardia's multivariate kurtosis as a locally optimal test statistic to detect outliers against equicorrelated distributions.     

Adolescents in residential treatment: The prevalence of ACEs,substance use and justice involvement

Abstract

Although associations between developmental trauma, juvenile justice involvement and youth substance use have been previously reported in the published literature, the interconnection among these three factors has not been adequately studied. This article describes the interconnection among these three factors and calls for greater attention to the role of adverse childhood experiences (ACEs) in the diagnosis and treatment of youth who present with histories of substance use and/or offending behavior. Pilot data are presented that show high rates of self-reported trauma exposure, substance use history, justice involvement, and mental health problems in a sample of adolescents in residential treatment. The data point to the need for residential treatment centers to consider trauma histories in developing treatment plans for youth with current and prior substance use and offending behavior.     

Marshall–Olkin q-Weibull distribution and max–min processes

In this paper, we introduce a new probability model known as Marshall–Olkin q-Weibull distribution. Various properties of the distribution and hazard rate functions are considered. The distribution is applied to model a biostatistical data. The corresponding time series models are developed to illustrate its application in times series modeling. We also develop different types of autoregressive processes with minification structure and max–min structure which can be applied to a rich variety of contexts in real life. Sample path properties are examined and generalization to higher orders are also made. The model is applied to a time series data on daily discharge of Neyyar river in Kerala, India.     

Assessing capacity in the setting of self-neglect: development of a novel screening tool for decision-making capacity

Compared with older adults with disabilities and those who autonomously choose to live in squalor, self-neglect syndrome arises from a predicate state of vulnerability in frail older adults. This state of vulnerability is characteristically associated with a decline in decision-making capacity regarding the ability to care for and protect oneself. We developed the COMP Screen to evaluate vulnerable older adults to identify potential gaps in decision-making capacity using a screening tool. A total of 182 older adults were evaluated and consistent declines in cognitive ability and decision-making processes were present in this population. However, there were no significant differences between elders referred for self-neglect and matched older adults. These findings suggest that declines in decision-making processes are not uncommon in vulnerable older adults but traditional conceptualizations of decision-making capacity may be inadequate for differentiating the capacity for self-care and protection in elders who self-neglect.