A Generalization of Geometric Brownian Motion with Applications

Although geometric Brownian motion has a great variety of applications, it can not cover all the random phenomena. The purpose of this article is to propose a model that generalizes geometric Brownian motion. We present some interesting applications of this model in financial engineering and statistical inferences for the unknown parameters.     

Estimation of discrete survival function for error‐prone diagnostic tests

The product limit or Kaplan‐Meier (KM) estimator is commonly used to estimate the survival function in the presence of incomplete time to event. Application of this method assumes inherently that the occurrence of an event is known with certainty. However, the clinical diagnosis of an event is often subject to misclassification due to assay error or adjudication error, by which the event is assessed with some uncertainty. In the presence of such errors, the true distribution of the time to first event would not be estimated accurately using the KM method. We develop a method to estimate the true survival distribution by incorporating negative predictive values and positive predictive values, into a KM‐like method of estimation. This allows us to quantify the bias in the KM survival estimates due to the presence of misclassified events in the observed data. We present an unbiased estimator of the true survival function and its variance. Asymptotic properties of the proposed estimators are provided, and these properties are examined through simulations. We demonstrate our methods using data from the Viral Resistance to Antiviral Therapy of Hepatitis C study.     

Calibrated predictions for multivariate competing risks models

Prediction models for time-to-event data play a prominent role in assessing the individual risk of a disease, such as cancer. Accurate disease prediction models provide an efficient tool for identifying individuals at high risk, and provide the groundwork for estimating the population burden and cost of disease and for developing patient care guidelines. We focus on risk prediction of a disease in which family history is an important risk factor that reflects inherited genetic susceptibility, shared environment, and common behavior patterns. In this work family history is accommodated using frailty models, with the main novel feature being allowing for competing risks, such as other diseases or mortality. We show through a simulation study that naively treating competing risks as independent right censoring events results in non-calibrated predictions, with the expected number of events overestimated. Discrimination performance is not affected by ignoring competing risks. Our proposed prediction methodologies correctly account for competing events, are very well calibrated, and easy to implement.     

Clarifying the Effect of Intellectual Capital on Performance: The Mediating Role of Dynamic Capability

Recent studies suggest a potential relationship between intellectual capital and dynamic capability in achieving performance. This is unsettling for managers because these studies contain little effort to develop a framework for understanding the relationship. To examine this unnerving potential, we develop and test a theoretical model that explains how dynamic capability mediates the impact of intellectual capital on performance. In this study, the scope of intellectual capital includes human capital, relational capital and structural capital. This study examines the pooled data of 242 high‐technology firms from 2001 to 2008. Results from Bayesian regression analysis suggest that the effect of structural capital on performance is completely mediated by dynamic capability. Furthermore, the findings show that dynamic capability does not completely mediate the respective effects of human capital and relational capital on performance, but does so only partially. These results provide convincing support for the importance of dynamic capability through accumulating R&D and marketing capability over time, thereby enhancing firm performance. The empirical findings and the ensuing discussion will be of interest to managers and practitioners.     

Reconsidering the exploration of power distance: an active case study approach

In this paper, we present what we call an “active case study” and we theorize specifically on the relevance hereof for an exploration of power distance (as conceptualized by Hofstede (Culture's Consequences: International Differences in Work-Related Values, Sage, Beverley Hills, CA, 1980)). As Hofstede conceives it, the notion of “power distance” enables an understanding of the predominant ideas about inequality prevalent in a (national) culture, which in turn infuse work relationships. An active case study approach—as we elucidate in the paper—implies an acknowledgement on the part of those organizing the research of the manner in which their intervention might affect the way “respondents” experience their work relationships. We suggest that our proposed active case study approach offers novel possibilities for exploring power distance and should be added to the repertoire of approaches used to examine this in organizational life. We develop our argument in this regard by offering a detailed account of the application of this approach to a Taiwanese organization (the Kaohsiung Harbor Bureau in Taiwan).     

Single-machine batch delivery scheduling with an assignable common due window

This paper addresses a batch delivery single-machine scheduling problem in which jobs have an assignable common due window. Each job will incur an early (tardy) penalty if it is early (tardy) with respect to the common due window under a given schedule. There is no capacity limit on each delivery batch, and the cost per batch delivery is fixed and independent of the number of jobs in the batch. The objective is to find the optimal size and location of the window, the optimal dispatch date for each job, as well as an optimal job sequence to minimize a cost function based on earliness, tardiness, holding time, window location, window size, and batch delivery. We show that the problem can be optimally solved in O(n⁸)$O(n^8)$ time by a dynamic programming algorithm under a reasonable assumption on the relationships among the cost parameters. A computational experiment is also conducted to evaluate the performance of the proposed algorithm. We also show that some special cases of the problem can be optimally solved by lower order algorithms.     

Boris Raux,olfactory narcissism,and environmental risk

This article develops the concept of “olfactory narcissism” through an analysis of Boris Raux’s multi-media artworks. Approaching narcissism through the sense of smell, Raux frames the self in insistently material, trans-corporeal terms. His olfactory projects thus explore questions about the porous self and the ethical and environmental stakes of “deodorization” and olfactory self-fashioning. The paper explores how olfactory aesthetics can contribute to a deeper understanding of modernity’s atmospheric practices as well as their implications for subject formation, social relations, and environmental health.     

Joint analysis of longitudinal data and competing terminal events in the presence of dependent observation times with application to chronic kidney disease

In many prospective clinical and biomedical studies, longitudinal biomarkers are repeatedly measured as health indicators to evaluate disease progression when patients are followed up over a period of time. Patient visiting times can be referred to as informative observation times if they are assumed to carry information in addition to that of the longitudinal biomarker measures alone. Irregular visiting times may reflect compliance with physician instruction, disease progression and symptom severity. When the follow-up time may be stopped by competing terminal events, it is possible that patient observation times may correlate with the competing terminal events themselves, thus making the observation times difficult to assess. To explicitly account for the impact of competing terminal events and dependent observation times on the longitudinal data analysis in the context of such complex data, we propose a joint model using latent random effects to describe the association among them. A likelihood-based approach is derived for statistical inference. Extensive simulation studies reveal that the proposed approach performs well for practical situations, and an analysis of patients with chronic kidney disease in a cohort study is presented to illustrate the proposed method.