Short run spc based upon the second order dynamic linear model for trend detection

Recently, several new applications of control chart procedures for short production runs have been introduced. Bothe (1989) and Burr (1989) proposed the use of control chart statistics which are obtained by scaling the quality characteristic by target values or process estimates of a location and scale parameter. The performance of these control charts can be significantly affected by the use of incorrect scaling parameters, resulting in either an excessive "false alarm rate," or insensitivity to the detection of moderate shifts in the process. To correct for these deficiencies, Quesenberry (1990, 1991) has developed the Q-Chart which is formed from running process estimates of the sample mean and variance. For the case where both the process mean and variance are unknown, the Q-chaxt statistic is formed from the standard inverse Z-transformation of a t-statistic. Q-charts do not perform correctly, however, in the presence of special cause disturbances at process startup. This has recently been supported by results published by Del Castillo and Montgomery (1992), who recommend the use of an alternative control chart procedure which is based upon a first-order adaptive Kalman filter model Consistent with the recommendations by Castillo and Montgomery, we propose an alternative short run control chart procedure which is based upon the second order dynamic linear model (DLM). The control chart is shown to be useful for the early detection of unwanted process trends. Model and control chart parameters are updated sequentially in a Bayesian estimation framework, providing the greatest degree of flexibility in the level of prior information which is incorporated into the model. The result is a weighted moving average control chart statistic which can be used to provide running estimates of process capability. The average run length performance of the control chart is compared to the optimal performance of the exponentially weighted moving average (EWMA) chart, as reported by Gan (1991). Using a simulation approach, the second order DLM control chart is shown to provide better overall performance than the EWMA for short production run applications     

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.     

Consolidation and fragmentation in environmental scanning: A review and research agenda

Environmental scanning is a broadly defined concept, having first received attention from scholars in the late 1960s. Over the years a number of similar and overlapping constructs have emerged in management literature. The aim of this study, via a systematic review and thematic analysis of relevant empirical research, is to consolidate foundation environmental scanning knowledge, demonstrate how scanning research has developed and fragmented over time, and propose an agenda for future research. The first contribution of our review is a new typology of environmental scanning research made up of five discrete research views, which provides a more comprehensive and contemporary overview of the field than previous studies. The second is a proposed agenda for future research that explicitly acknowledges the role of technology, an area that is presently underdeveloped in foundation scanning literature. The third contribution is to signpost future directions for research on scanning and organisational performance using a number of theoretical perspectives. The overall outcome of our review is to move scanning research on from increasingly incremental contributions concerned with context to a place where the changing role of technology and the mechanisms through which environmental scanning contributes to competitive advantage can be more thoroughly understood.     

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).     

Longitudinal Prediction of Mid‐Adolescent Psychosocial Outcomes From Early Adolescent Family Help Seeking and Family Support

This study examined whether family help seeking and family support represented the same or distinct constructs and prospective associations between emergent constructs and psychosocial outcomes. Data were from 1,713 school‐based adolescents participating in a randomized controlled trial, in Victoria, Australia. Family help seeking emerged as a single factor, distinct from family support, and was prospectively associated with improved psychosocial outcomes. Father closeness predicted lower depressive symptoms. Family help seeking predicted higher help seeking for peers. Interactions between family help seeking and family support on psychosocial outcomes were not apparent. Findings highlight the importance of examining family help seeking and family support separately in future studies of adolescents' help‐seeking behavior.