Quantile regression models for current status data

Current status data arise frequently in demography, epidemiology, and econometrics where the exact failure time cannot be determined but is only known to have occurred before or after a known observation time. We propose a quantile regression model to analyze current status data, because it does not require distributional assumptions and the coefficients can be interpreted as direct regression effects on the distribution of failure time in the original time scale. Our model assumes that the conditional quantile of failure time is a linear function of covariates. We assume conditional independence between the failure time and observation time. An M-estimator is developed for parameter estimation which is computed using the concave-convex procedure and its confidence intervals are constructed using a subsampling method. Asymptotic properties for the estimator are derived and proven using modern empirical process theory. The small sample performance of the proposed method is demonstrated via simulation studies. Finally, we apply the proposed method to analyze data from the Mayo Clinic Study of Aging

QUANTILE REGRESSION MODELS FOR INTERVAL-CENSORED FAILURE TIME DATA

Quantile regression models the conditional quantile as a function of independent variables providing a complete association between the response and predictors. Quantile regression can describe the association at different quantiles yielding more information than the least squares method which only detects associations with the conditional mean. Quantile regression models have gained popularity in many disciplines including medicine, finance, economics, and ecology as they can accommodate heteroscedasticity. A specific type of failure time data is called interval-censored where the failure time is only known to have occurred between certain observation times. Such data appears commonly in medical or longitudinal studies because disease onset is known to have occurred between scheduled visits but the exact time is unknown. Quantile regression has been extended to survival analysis with random censoring time. Most methods focus on survival analysis with right-censored data while a few were developed for data with other censoring mechanisms. Despite the fact that the development for censored quantile regression flourishes, limited work has been done to handle interval-censored failure time data under the quantile regression framework. In this dissertation, we developed a new method to analyze interval-censored failure time data using conditional quantile regression models. Our method can handle both Case I and Case II interval-censored data and allow the censoring time to depend on covariates. We developed an estimation procedure that is computationally efficient and easy to implement with inference performed using a subsampling method. The consistency and asymptotic distribution of the resulting estimators were established using modern empirical process theory. The developed method was extended as a computational tool to analyze interval-censored data for accelerated failure time models. The estimators from different quantiles were combined to increase the efficiency of the estimators. The small sample performances were demonstrated via simulation studies. The proposed methods were illustrated with current status datasets, data from the Voluntary HIV-1 Counseling and Testing Efficacy Study Group and calcification study, and Case II interval-censored data, data from the Atherosclerosis Risk in Communities Study and breast cosmesis data.Doctor of Philosoph

Expressions of Hippocampal Mineralocorticoid Receptor (MR) and Glucocorticoid Receptor (GR) in the Single-Prolonged Stress-Rats

Post-traumatic stress disorder (PTSD) is a stress-related mental disorder caused by traumatic experience. Single-prolonged stress (SPS) is one of the animal models proposed for PTSD. Rats exposed to SPS showed enhanced inhibition of the hypothalamo-pituitary-adrenal (HPA) axis, which has been reliably reproduced in patients with PTSD. Mineralocorticoid receptor (MR) and glucocorticoid receptor (GR) in the hippocampus regulate HPA axis by glucocorticoid negative feedback. Abnormalities in negative feedback are found in PTSD, suggesting that GR and MR might be involved in the pathophysiology of these disorders

How to Retain Consumers: A Trust-Commitment Model

Although studies on the determinants of consumers’ continuance intention in e-marketplaces have grown in recent years, the research is predominantly related to unidimensional trust and commitment. In this research, the authors focus on the distinct roles of different types of consumer trust and commitment on consumers’ continuance intention. Drawing upon organizational commitment and trust theories, we develop a continuance intention model that includes two types of trust and two types of commitments. We collected a sample of 287 online consumers to validate the theoretical model. Our data suggest that consumers’ trust and commitment positively affect their continuance intention. Our study also indicates that the psychological states underlying the commitments are different. Key findings and implications are discussed

Ultrafast all-optical switching via coherent modulation of metamaterial absorption

We report on the demonstration of a femtosecond all-optical modulator providing, without nonlinearity and therefore at arbitrarily low intensity, ultrafast light-by-light control. The device engages the coherent interaction of optical waves on a metamaterial nanostructure only 30 nm thick to efficiently control absorption of near-infrared (750-1040 nm) femtosecond pulses, providing switching contrast ratios approaching 3:1 with a modulation bandwidth in excess of 2 THz. The functional paradigm illustrated here opens the path to a family of novel meta-devices for ultra-fast optical data processing in coherent networks.Comment: 5 pages, 4 figure

Value Contributed by Education in IT Firms

An educated workforce is critical to IT firms’ ability to innovate and compete in the market. Surprisingly, there is very little research on how education contributes to the profitability of IT firms and how educated employees contribute to a firms’ research and development activities. Using theories from human capital literature, we propose a model to measure how aggregate firm level education impacts firms’ profits in IT industries and how the relation is moderated by a firm’s R&D investments. Our results suggest that education is associated with a positive firm performance in IT industries. We also show that the interaction effects between R&D and education is positive, suggesting that IT firms which invest in highly skilled employees are in a better position to take advantage of R&D investments. This paper adds several new insights to the literature on human capital and firm performance