Journal of Data Science

Abstract: For the first time, we propose and study the Kumaraswamy generalized half-normal distribution for modeling skewed positive data. The half-normal and generalized half-normal (Cooray and Ananda, 2008) distributions are special cases of the new model. Various of its structural properties are derived, including explicit expressions for the density function, moments, generating and quantile functions, mean deviations and moments of the order statistics. We investigate maximum likelihood estimation of the parameters and derive the expected information matrix. The proposed model is modified to open the possibility that long-term survivors may be presented in the data. Its applicability is illustrated by means of four real data sets.

In this paper, a new version of the Poisson Lomax distributions is proposed and studied. The new density is expressed as a linear mixture of the Lomax densities. The failure rate function of the new model can be increasing-constant, increasing, U shape, decreasing and upside down-increasing. The statistical properties are derived and four applications are provided to illustrate the importance of the new density. The method of maximum likelihood is used to estimate the unknown parameters of the new density. Adequate fitting is provided by the new model.

Abstract: This paper discusses a comprehensive statistical approach that will be useful in answering health-related questions concerning mortality and incidence rates of chronic diseases such as cancer and hypertension. The developed spatio-temporal models will be useful to explain the patterns of mortality rates of chronic disease in terms of environmental changes and social-economic conditions. In addition to age and time effects, models include two components of normally distributed residual effects and spatial effects, one to represent average regional effects and another to represent changes of subgroups within region over time. Numerical analysis is based on male lung cancer mortality data from the state of Missouri. Gibbs sampling is used to obtain the posterior quantities. As a result, all models discussed in this article fit well in stabilizing the mortality rates, especially in the less populated areas. Due to the richness of hierarchical settings, easy interpretation of parameters and ease of implementation, any models proposed in this paper can be applied generally to other sets of data.

Abstract: We consider the Autoregressive Conditional Marked Duration (ACMD) model and apply it to 16 stocks traded in Hong Kong Stock Ex change (SEHK). By examining the orderings of appropriate sets of model parameters, market microstructure phenomena can be explained. To sub stantiate these conclusions, likelihood ratio test is used for testing the sig nificance of the parameter orderings of the ACMD model. While some of our results resolve a few controversial market microstructure hypotheses and echo some of the existing empirical evidence, we discover some interesting market microstructure phenomena that may be characteristic to SEHK.

Abstract: Believe the Positive (BP) and Believe the Negative (BN) rules for combining two continuous diagnostic tests are compared with proce dures based on likelihood ratio and linear combination of the two tests. The sensitivity-specificity relationship for BP/BN is illustrated through a graph ical presentation of a ”ROC surface”, which leads to a natural approach of choosing between BP and BN. With a bivariate normal model, it is shown that the discriminating power of this approach is higher when the correla tion between the two tests has different signs for non-diseased and diseased population, given the location and variations of the two distributions are fixed. The idea is illustrated through an example.

Abstract: Household data are frequently used in estimating vaccine efficacy because it provides information about every individual’s exposure to vaccinated and unvaccinated infected household members. This information is essential for reliable estimation of vaccine efficacy for infectiousness (V EI ), in addition to estimating vaccine efficacy for susceptibility (V ES ). However, accurate infection outcome data is not always available on each person due to high cost or lack of feasible methods to collect this information. Lack of reliable data on true infection status may result in biased or inefficient estimates of vaccine efficacy. In this paper, a semiparametric method that uses surrogate outcome data and a validation sample is introduced for estimation of V ES and V EI from a sample of households. The surrogate outcome data is usually based on illness symptoms. We report the results of simulations conducted to examine the performance of the estimates, compare the proposed semiparametric method with maximum likelihood methods that either use the validation data only or use the surrogate data only and address study design issues. The new method shows improved precision as compared to a method based on the validation sample only and smaller bias as compared to a method using surrogate outcome data only. In addition, the use of household data is shown to greatly improve the attenuation in the estimate of V ES due to misclassification of the outcome, as compared to the use of a random sample of unrelated individuals.

Abstract: The generalized gamma model has been used in several applied areas such as engineering, economics and survival analysis. We provide an extension of this model called the transmuted generalized gamma distribution, which includes as special cases some lifetime distributions. The proposed density function can be represented as a mixture of generalized gamma densities. Some mathematical properties of the new model such as the moments, generating function, mean deviations and Bonferroni and Lorenz curves are provided. We estimate the model parameters using maximum likelihood. We prove that the proposed distribution can be a competitive model in lifetime applications by means of a real data set.

This study applied partial least squares (PLS) path modeling for quantifying and identifying the determinants of job seekers’ acceptance and use of employment websites (EWs) by using an aggregate model that applied task-technology fit (TTF), consumer acceptance and use of information technology (UTAUT2). We propose that the most crucial constructs explaining EW adoption are habit, behavioral intention, performance expectancy, and facilitating conditions. This study verified that a job seeker’s habits were a major predictor of intention and usage of EWs involving web-based technology and occasional usage. Thus, when job seekers perceive that their task is to fit the technology, they recognize the value of using the technology and use it habitually.