Simultaneous statistical modelling of excess zeros, over/underdispersion, and multimodality with applications in hotel industry.

We propose zero-inflated statistical models based on the generalized Hermite distribution for simultaneously modelling of excess zeros, over/underdispersion, and multimodality. These new models are parsimonious yet remarkably flexible allowing the covariates to be introduced directly through the mean, dispersion, and zero-inflated parameters. To accommodate the interval inequality constraint for the dispersion parameter, we present a new link function for the covariate-dependent dispersion regression model. We derive score tests for zero inflation in both covariate-free and covariate-dependent models. Both the score test and the likelihood-ratio test are conducted to examine the validity of zero inflation. The score test provides a useful tool when computing the likelihood-ratio statistic proves to be difficult. We analyse several hotel booking cancellation datasets extracted from two recently published real datasets from a resort hotel and a city hotel. These extracted cancellation datasets reveal complex features of excess zeros, over/underdispersion, and multimodality simultaneously making them difficult to analyse with existing approaches. The application of the proposed methods to the cancellation datasets illustrates the usefulness and flexibility of the models.

Is Insomnia a Risk Factor for Decreased Influenza Vaccine Response?

Healthy young adult college students (N = 133) with Insomnia (n = 65) or No Insomnia (n = 68) were compared on influenza serum antibody levels pre- and four weeks postvaccination. Volunteers underwent structured clinical interviews for sleep disorders to ensure insomnia diagnoses, as well as psychiatric interviews, physical examinations, and drug testing to ensure comorbid health problems were not potential confounds. There were significant time (both groups had increases in antibody levels pre- to postvaccination) and group (Insomnia group had lower HI antibody levels overall) main effects, but the time × group interaction was nonsignificant. Exploratory analyses did find significant PSQI x Time (p < .001) and Insomnia Status × Time (p = .002) interaction effects. Results indicate insomnia may be a risk factor for lowered immunity to the influenza virus.

Globally convergent algorithms for estimating generalized gamma distributions in fast signal and image processing.

Many applications in real-time signal, image, and video processing require automatic algorithms for rapid characterizations of signals and images through fast estimation of their underlying statistical distributions. We present fast and globally convergent algorithms for estimating the three-parameter generalized gamma distribution (G Gamma D). The proposed method is based on novel scale-independent shape estimation (SISE) equations. We show that the SISE equations have a unique global root in their semi-infinite domains and the probability that the sample SISE equations have a unique global root tends to one. The consistency of the global root, its scale, and index shape estimators is obtained. Furthermore, we establish that, with probability tending to one, Newton-Raphson (NR) algorithms for solving the sample SISE equations converge globally to the unique root from any initial value in its given domain. In contrast to existing methods, another remarkable novelty is that the sample SISE equations are completely independent of gamma and polygamma functions and involve only elementary mathematical operations, making the algorithms well suited for real-time both hardware and software implementations. The SISE estimators also allow the maximum likelihood (ML) ratio procedure to be carried out for testing the generalized Gaussian distribution (GGD) versus the G Gamma D. Finally, the fast global convergence and accuracy of our algorithms for finite samples are demonstrated by both simulation studies and real image analysis.

A trend analysis of organ transplantation among ethnic groups.

PURPOSE: This research provides public policy implications regarding organ resource allocation and increases public awareness of the current status of transplant use in various ethnic populations. PROCEDURES: Healthcare Cost and Utilization Project (HCUP), National Inpatient Sample (NIS) data were used to obtain a yearly estimate of the number of organ transplants by organ and by ethnic origin for 1988-1997. ICD-9-CM codes identified lung, heart, liver, and kidney organ-transplantation procedures. Each record in the sample was weighted by its respective discharge weight in order to extrapolate a national estimate. To assess whether there are significant differences among ethnic groups in organ transplantation rates over time, regression models were estimated for heart, liver, and kidney transplants. Transplantation rates were modeled as a function of time, ethnic origin, and interaction variables. FINDINGS: Examination of time trend graphs and regression analyses indicates that transplantation rates have not varied substantially across ethnic groups between 1988 and 1997. Rates for all groups, with the exception of Asians, exhibited similar time trends with little systematic variation. CONCLUSIONS: Further research is needed to determine whether variations exist due to organ availability versus prevalence of the disease.