Journal of Data Science

Abstract: Contraception is not commonly used by Omani women because of socio-cultural traditions, religious beliefs and poor knowledge but among the users modern contraceptive methods are more popular than traditional methods. Multilevel analysis is conducted to investigate associations between individual and religion level characteristics and different type of contraceptive method and to obtain a better understanding of the factors associated with contraceptive method choices used by 15-49 years women in Oman using Oman National Reproductive Health Survey data. The results confirm the importance of individual’s own characteristics have enduring effects on contraceptive method choices and it is found that for a given individual, contraceptive method choice varies across women’s age, education level and their number of living children. We have found considerable differences in the results of the estimates between single and multilevel approaches.

Abstract: The generalized Poisson regression model has been used to model dispersed count data. It is a good competitor to the negative binomial regression model when the count data is over-dispersed. Zero-inflated Poisson and zero-inflated negative binomial regression models have been proposed for the situations where the data generating process results into too many zeros. In this paper, we propose a zero-inflated generalized Poisson (ZIGP) regression model to model domestic violence data with too many zeros. Estimation of the model parameters using the method of maximum likelihood is provided. A score test is presented to test whether the number of zeros is too large for the generalized Poisson model to adequately fit the domestic violence data

Bayesian hierarchical regression (BHR) is often used in small area estimation (SAE). BHR conditions on the samples. Therefore, when data are from a complex sample survey, neither survey sampling design nor survey weights are used. This can introduce bias and/or cause large variance. Further, if non-informative priors are used, BHR often requires the combination of multiple years of data to produce sample sizes that yield adequate precision; this can result in poor timeliness and can obscure trends. To address bias and variance, we propose a design assisted model-based approach for SAE by integrating adjusted sample weights. To address timeliness, we use historical data to define informative priors (power prior); this allows estimates to be derived from a single year of data. Using American Community Survey data for validation, we applied the proposed method to Behavioral Risk Factor Surveillance System data. We estimated the prevalence of disability for all U.S. counties. We show that our method can produce estimates that are both more timely than those arising from widely-used alternatives and are closer to ACS’ direct estimates, particularly for low-data counties. Our method can be generalized to estimate the county-level prevalence of other health related measurements.

Abstract: Early phase clinical trials may not have a known variation (σ) for the response variable. In the light of applying t-test statistics, several procedures were proposed to use the information gained from stage-I (pilot study) to adaptively re estimate the sample size for managing the overall hypothesis test. We are interested in choosing a reasonable stage-I sample size (m) towards achieving an accountable overall sample size (stage-I and later). Conditional on any specified m, this paper replaces σ by the estimated σ (from stage-I with sample size m) to use the conventional formula under normal distribution assumption to re-estimate an overall sample size. The estimated σ, re-estimated overall sample size and the collective information (stage-I and later) would be incorporated into a surrogate normal variable which undergoes hypothesis test based on standard normal distribution. We plot the actual type I&II error rates and the expected sample size against m in order to choose a good universal stage-I sample size (𝑚∗ ) to start

Abstract: This paper is concerned with the change point analysis in a general class of distributions. The quasi-Bayes and likelihood ratio test procedures are considered to test the null hypothesis of no change point. Exact and asymptotic behaviors of the two test statistics are derived. To compare the performances of two test procedures, numerical significance levels and powers of tests are tabulated for certain selected values of the parameters. Estimation of the change point based on these two test procedures are also considered. Moreover, the epidemic change point problem is studied as an alternative model for the single change point model. A real data set with epidemic change model is analyzed by two test procedures.

Abstract: A statistical evaluation of the Baltimore County water well database is performed to gain insight on the sustainability of domestic supply wells in crystalline bedrock aquifers over the last 15 years. Variables potentially related to well yield that are considered included well construction, geol ogy, well depth, and static water level. A variety of statistical methods are utilized to assess correlation and significance from a database of approxi mately 8,500 wells, and a logistic regression model is developed to predict the probability of well failure by geology type. Results of a two-way analysis of variance technique indicate that the average well depth and yield are sta tistically different among the established geology groups, and between failed and non-failed wells. The static water level is shown to be statistically dif ferent among the geology groups but not among failed and non-failed wells. A logistic regression model results that well yield is the most influential vari able for predicting well failure. Static water level and well depth was not found to be significant in predicting well failure.

Abstract: Shared frailty models are often used to model heterogeneity in survival analysis. The most common shared frailty model is a model in which hazard function is a product of random factor (frailty) and baseline hazard function which is common to all individuals. There are certain as sumptions about the baseline distribution and distribution of frailty. Mostly assumption of gamma distribution is considered for frailty distribution. To compare the results with gamma frailty model, we introduce three shared frailty models with generalized exponential as baseline distribution. The other three shared frailty models are inverse Gaussian shared frailty model, compound Poisson shared frailty model and compound negative binomial shared frailty model. We fit these models to a real life bivariate survival data set of McGilchrist and Aisbett (1991) related to kidney infection using Markov Chain Monte Carlo (MCMC) technique. Model comparison is made using Bayesian model selection criteria and a better model is suggested for the data.

Brand Cluster is proposed based on the background of evolved consumption modes and concepts as well as brand preferences of different categories of consumers. With the support of inter-urban, inter-category and inter-brand big data, after deep learning and profound analysis of consumption relations of different brands, Brand Cluster was born to reflect characteristics of diverse consumers. We try to understand the inner features of 18 clusters of brands and how these clusters look like in different cities, which underlies the practice of city siting of brand owners. Brand Cluster is believed to reveal the relationships between “allies” of brands in a whole new angel of view and in the large. In addition, the make-up of brand clusters in different cities indicate whether a new city is appropriate for brand owners to expand into.

Abstract: A statistical approach, based on artificial neural networks, is pro posed for the post-calibration of weather radar rainfall estimation. Tested artificial neural networks include multilayer feedforward networks and radial basis functions. The multilayer feedforward training algorithms consisted of four variants of the gradient descent method, four variants of the conju gate gradient method, Quasi-Newton, One Step Secant, Resilient backprop agation, Levenberg-Marquardt method and Levenberg-Marquardt method using Bayesian regularization. The radial basis networks were the radial basis functions and the generalized regression networks. In general, results showed that the Levenberg-Marquardt algorithm using Bayesian regulariza tion can be introduced as a robust and reliable algorithm for post-calibration of weather radar rainfall estimation. This method benefits from the conver gence speed of the Levenberg-Marquardt algorithm and from the over fitting control of Bayes’ theorem. All the other multilayer feedforward training al gorithms result in failure since they often lead to over fitting or converged to a local minimum, which prevents them from generalizing the data. Radial basis networks are also problematic since they are very sensitive when used with sparse data.