Journal of Data Science

Abstract: In this article, we present a joint modeling approach that com bines information from multiple diseases. Our model can be used to obtain more reliable estimates in rare diseases by incorporating information from more common diseases for which there exists a shared set of important risk factors. Information is shared through both a latent spatial process and a latent temporal process. We develop a fully Bayesian hierarchical imple mentation of our spatio-temporal model in order to estimate relative risk, adjusted for age and gender, at the county level in Iowa in five-year intervals for the period 1973–2002. Our analysis includes lung, oral, and esophageal cancers which are related to excessive tobacco and alcohol use risk factors. Lung cancer risk estimates tend to be stable due to the large number of occurrences in small regions, i.e. counties. The lower risk cancers (oral and esophageal) have fewer occurrences in small regions and thus have estimates that are highly variable and unreliable. Estimates from individual and joint modeling of these diseases are examined and compared. The joint modeling approach has a profound impact on estimates regarding the low risk oral and esophageal cancers while the higher risk lung cancer is minutely impacted. Clearer spatial and temporal patterns are obtained and the standard errors of the estimates are reduced leading to more reliable estimates.

Abstract: The paper presents a statistical analysis of electricity spot prices in a deregulated market in New South Wales, Australia, in the period 10 May, 1996 – 7 March, 1998. It is unusual that a single set of data, such as this, allows one to consider a relatively systematic sequence of statistical problems, each resulting in clear, although not always obvious, solutions. This is the reason why these data and their analysis can be used as a rel atively good base for training in practical statistical analysis. Existing for merly as a report, the material has been used in lecture courses in several universities in Australia and New Zealand.

In this paper, kumaraswamy reciprocal family of distributions is introduced as a new continues model with some of approximation to other probabilistic models as reciprocal, beta, uniform, power function, exponential, negative exponential, weibull, rayleigh and pareto distribution. Some fundamental distributional properties, force of mortality, mills ratio, bowley skewness, moors kurtosis, reversed hazard function, integrated hazard function, mean residual life, probability weighted moments, bonferroni and lorenz curves, laplace-stieltjes transform of this new distribution with the maximum likelihood method of the parameter estimation are studied. Finally, four real data sets originally presented are used to illustrate the proposed estimators.

Abstract: This article displays an application of the statistical method motivated by Bruno de Finetti’s operational subjective theory of probability. We use exchangeable forecasting distributions based on mixtures of linear combinations of exponential power (EP) distributions to forecast the sequence of daily rates of return from the Dow-Jones index of stock prices over a 20 year period. The operational subjective statistical method for comparing distributions is quite different from that commonly used in data analysis, because it rejects the basic tenets underlying the practice of hypothesis testing. In its place, proper scoring rules for forecast distributions are used to assess the values of various forecasting strategies. Using a logarithmic scoring rule, we find that a mixture linear combination of EP distributions scores markedly better than does a simple mixture over the EP family, which scores much better than does a simple Normal mixture. Surprisingly, a mixture over a linear combination of three Normal distributions also makes a substantial improvement over a simple Normal mixture, although it does not quite match the performance of even the simple EP mixture. All substantive forecasting improvements become most marked after extreme tail phenomena were actually observed in the sequence, in particular after the abrupt drop in market prices in October, 1987. However, the improvements continue to be apparent over the long haul of 1985-2006 which has seen a number of extreme price changes. This result is supported by an analysis of the Negentropies embedded in the forecasting distributions, and a proper scoring analysis of these Negentropies as well.

Abstract: We have extended some previous works by applying the product partition model (PPM) to identify multiple change points in the variance of normal data sequence assuming mean equal to zero. This type of problem is very common in applied economics and finance. We consider the Gibbs sampling scheme proposed in the literature to obtain the posterior estimates or product estimates for the variance and the posterior distributions for the instants when changes take place and also for the number of change points in the sequence. The PPM is used to obtain the posterior behavior of the volatility (measured as the variance) in the series of returns of four important Latin American stock indexes (MERVAL-Argentina, IBOVESPABrazil, IPSA-Chile and IPyC-Mexico). The posterior number of change point as well as the posterior most probable partition for each index series are also obtained.

Abstract: Abstract: Partial least squares (PLS) method has been designed for handling two common problems in the data that are encountered in most of the applied sciences including the neuroimaging data: 1) Collinearity problem among explanatory variables (X) or among dependent variables (Y); 2) Small number of observations with large number of explanatory variables. The idea behind this method is to explain as much as possible covariance between two blocks of X and Y variables by a small number of uncorrelated variables. Apart from the other applied sciences in which PLS are used, in the application of imaging data PLS has been used to identify task dependent changes in activity, changes in the relations between brain and behavior, and to examine functional connectivity of one or more brain regions. The aim of this paper is to give some information about PLS and apply on electroencephalography (EEG) data to identify stimulation dependent changes in EEG activity.

Abstract: A total of 1094 HIV patients were involved in a cohort study (from January-December 2010) with follow-up in their CD4 cell transition counts and grouped according to their immunological states into five(5) states developed by Guiseppe Di Biase et al (2007). The five states (5) considered were: State one (CD4 > 500 cells/mm3 ), State two (350 < CD4 500 cells /mm3 ) State three(200 < CD4 350 cells/mm3 ), State four(CD4 200 cells/mm3 ), State five(Death). These states de ne the seriousness of the sickness based on the epidemiological states of the patients CD4 cell counts. We use the non-stationary Markov chain model for the prediction. The estimation of the non-stationary probabilities were done using the exponential smoothing technique. The result of the prediction showed a gradual decrease of the CD4 cells as we move from Jan-Dec. Furthermore, the result showed that the patients in the study cannot survive death from the month Dec. 2011, if they are not subjected to therapy, using highly active antiretrovirals (HAART). The results also showed that the model can be used for the testing of the drug e efficacy administered to patients within a given period.

This paper discusses the coherent forecasting in two types of integervalued geometric autoregressive time series models of order one, viz., Geometric Integer-valued Autoregressive (GINAR(1)) model and New Geometric Integer-valued Autoregressive (NGINAR(1)) model. GINAR(1) model uses binomial thinning for the process generation, whereas, NGINAR(1) uses negative binomial thinning. The k-step ahead conditional probability mass function and the corresponding probability generating functions are derived. It is observed that for higher order lags, the conditional mean, variance and the probability generating functions of these two processes are close to each other, whereas, for lower order lags, they differ. The coherent forecasting performance of these models is studied with the help of simulated and real data sets.