Review of Statistical Filmstrip Programs

We develop a novel nonparametric likelihood ratio test for independence between two random variables using a technique that is free of the common constraints of defining a given set of specific dependence structures. Our methodology revolves around an exact density-based empirical likelihood ratio test statistic that approximates in a distribution-free fashion the corresponding most powerful parametric likelihood ratio test. We demonstrate that the proposed test is very powerful in detecting general structures of dependence between two random variables, including nonlinear and/or random-effect dependence structures. An extensive Monte Carlo study confirms that the proposed test is superior to the classical nonparametric procedures across a variety of settings. The real-world applicability of the proposed test is illustrated using data from a study of biomarkers associated with myocardial infarction. Supplementary materials for this article are available online.     

Recursive Schemes for Calculating Common Cumulative Distributions

This article gives a method for obtaining accurate (5 decimal places) estimates of nine common cumulative distributions. Starting with a positive series expansion, we use the common ratio of each term to the preceding term and proceed as with a geometric series (the ratio may involve the term number). This avoids calculating terms in the the numerator or denominator which can be large enough to overflow or small enough to underflow the machine. The method is fast because it eliminates the necessity of calculating each term of the series in its entirety.     

Segmentation,alignment and statistical analysis of biosignals with application to disease classification

We present a novel methodology for a comprehensive statistical analysis of approximately periodic biosignal data. There are two main challenges in such analysis: (1) the automatic extraction (segmentation) of cycles from long, cyclostationary biosignals and (2) the subsequent statistical analysis, which in many cases involves the separation of temporal and amplitude variabilities. The proposed framework provides a principled approach for statistical analysis of such signals, which in turn allows for an efficient cycle segmentation algorithm. This is achieved using a convenient representation of functions called the square-root velocity function (SRVF). The segmented cycles, represented by SRVFs, are temporally aligned using the notion of the Karcher mean, which in turn allows for more efficient statistical summaries of signals. We show the strengths of this method through various disease classification experiments. In the case of myocardial infarction detection and localization, we show that our method compares favorably to methods described in the current literature.     

Robust Correlation Structure for Multivariate Failure Time Data

When incomplete repeated failure times are collected from a large number of independent individuals, interest is focused primarily on the consistent and efficient estimation of the effects of the associated covariates on the failure times. Since repeated failure times are likely to be correlated, it is important to exploit the correlation structure of the failure data in order to obtain such consistent and efficient estimates. However, it may be difficult to specify an appropriate correlation structure for a real life data set. We propose a robust correlation structure that can be used irrespective of the true correlation structure. This structure is used in constructing an estimating equation for the hazard ratio parameter, under the assumption that the number of repeated failure times for an individual is random. The consistency and efficiency of the estimates is examined through a simulation study, where we consider failure times that marginally follow an exponential distribution and a Poisson distribution is assumed for the random number of repeated failure times. We conclude by using the proposed method to analyze a bladder cancer dataset.     

Now and again: Eventful experience as a resource in senior activism

ABSTRACT

Shared memories shape relations among social movement participants and their organizations. However, scholars often ignore how experience operates as a means of solidifying attachment in group contexts. In contrast, I argue that activism depends on how participants publicly recall events. In this, I integrate a social memory perspective with the examination of activist movements. Through narrative, participants build engagement by presenting the self-in-history as a model for collective action. I refer to this as eventful experience, utilizing memorable moments as a resource for generating commitment. Movements depend upon members communicating the critical moments of their lives, embedding personal timelines in group culture. The linkage of personal experience and public events is a strategy by which individuals motivate collective action. Drawing on a thirty-month ethnography of a progressive senior citizen activist group in Chicago, I examine how members use an awareness of temporality to build a culture of action. Each movement group uses the past experiences of participants to build their culture – what Jasper refers to as taste in tactics, incorporating past successes, present plans, and imagined futures into a call for direct action.     

Career Decision State and Assessment of RIASEC Interest Structure

The career decision state (CDS) was studied in relation to RIASEC (Realistic, Investigative, Artistic, Social, Enterprising, Conventional) profiles in Self-Directed Search results among college students (86 women, 27 men). Three CDS dimensions measured by the Career State Inventory (CSI; certainty, satisfaction, and clarity) were examined as predictors of RIASEC primary and secondary constructs. Significant, albeit modest, zero-order correlations (p < .05) emerged between clarity and 3 secondary constructs (i.e., consistency, commonness, and coherence). Results of regression analyses indicated that CSI dimensions predicted 1 secondary construct (i.e., commonness).     

Effects of Leader Support in the Work Unit on the Relationship Between Work Spillover and Family Adaptation

This study examines the direct versus the buffering effect of leader support in the work unit on the relationship between work spillover and family adaptation. The analyses use data from a probability sample of 3,190 married soldiers in the U.S. Army who participated in the 1989 Army and Family Survey, and the data are analyzed by the gender of the respondent. Two types of work spillover are examined in the analysis (energy and time interference), and both internal and external types of family adaptation are hypothesized and supported by the empirical analysis. Only modest support is found for the buffering effect hypothesis. In support of the direct effect hypothesis, the findings indicate that leader support in the work unit decreases perceptions of work spillover, which is a preventive effect, and enhances perceptions of external adaptation, which is a therapeutic effect.     

Restaurant Revenue Management at Chevys: Determining the Best Table Mix

Revenue management has been used in a variety of industries and generally takes the form of managing demand by manipulating length of customer usage and price. Supply mix is rarely considered, although it can have considerable impact on revenue. In this research, we focused on developing an optimal supply mix, specifically on determining the supply mix that would maximize revenue. We used data from a Chevys restaurant, part of a large chain of Mexican restaurants, in conjunction with a simulation model to evaluate and enumerate all possible supply (table) mixes. Compared to the restaurant's existing table mix, the optimal mix is capable of handling a 30% increase in customer volume without increasing waiting times beyond their original levels. While our study was in a restaurant context, the results of this research are applicable to other service businesses.     

Predicting Adolescent Autonomy from Parents: Relationship Connectedness and Restrictiveness

This study examined the extent to which adolescents'behavioral autonomy was predicted by several aspects of the parent-youth relationship that are encompassed by the general constructs connectedness and restrictiveness. Both of these general relationship constructs are composed of more specific social-psychological predictors consisting of parental behaviors, parent-adolescent authority dimensions, and indicators of family ties. A total of 657 adolescents (mean age = 16.3 years) and 753 parents responded to self-report questionnaires. Hierarchical multiple regression analyses were used to test the hypotheses from both the adolescents'and parents'perspectives in separate models. Many of the predictions were confirmed, indicating that adolescent behavioral autonomy often develops within contexts of relationship connectedness, such as continuing parent-youth authority and supportiveness. Moreover, as expected, youthful autonomous behavior was inhibited by such aspects of relationship restrictiveness as punitive behavior and the perceived coercive abilities of parents.