Journal of Data Science

Abstract: this paper provides a novel research on the pricing ability of the hybrid ANNs based upon the Hang Seng Index Options spanning a period of from Nov, 2005 to Oct, 2011, during which time the 2007-20008 financial crisis had developed. We study the performances of two hybrid networks integrated with Black-Scholes model and Corrado and Su model respectively. We find that hybrid neural networks trained by using the financial data retained from a booming period of a market cannot have good predicting performance for options for the period that undergoes a financial crisis (tumbling period in the market), therefore, it should be cautious for researchers/practitioners when carry out the predictions of option prices by using hybrid ANNs. Our findings have likely answered the recent puzzles about NN models regarding to their counterintuitive performance for option pricing during financial crises, and suggest that the incompetence of NN models for option pricing is likely due to the fact NN models may have been trained by using data from improper periods of market cycles (regimes), and is not necessarily due to the learning ability and the flexibility of NN models.

Abstract: This paper uses a structural time series methodology to test the notion of interconnectedness between the UK and the US credit markets. The empirical tests utilise data on premium for the Banking sector credit default swaps (CDS) and covers the recent period of financial turmoil. The methodology based on Kalman filter is robust in the presence of limited convergence. The long-term steady state convergence in CDS premium is clearly noticeable between these two markets from the results. This observation lends support for the coordinated regulatory policy initiatives to deal with the crisis and offer suggestions for sound operations of the international financial systems.

Abstract: Identifying influential observations is an important part of the model building process in linear regression. There are numerous diagnostic measures based on different approaches in linear regression analysis. However, the problem of multicollinearity and influential observations may occur simultaneously. Therefore, we propose new diagnostic measures based on the two parameter ridge estimator defined by Lipovetsky and Conklin (2005) alternative to the usual ridge regression and ordinary linear regression. We define two parameter ridge-type generalizations of DFFITS and Cook’s distance. Moreover, we obtain approximate case deletion formulas and provide approximate versions of new measures. Finally, we illustrate the benefits of proposed measures in real data examples.

Abstract: In this small note we have established some new explicit expressions for ratio and inverse moments of lower generalized order statistics for the Marshall-Olkin extended Burr type XII distribution. These explicit expressions can be used to develop the relationship for moments of ordinary order statistics, record statistics and other ordered random variable techniques. Further, a characterization result of this distribution has been considered on using the conditional moment of the lower generalized order statistics.

Abstract: Scientific interest often centers on characterizing the effect of one or more variables on an outcome. While data mining approaches such as random forests are flexible alternatives to conventional parametric models, they suffer from a lack of interpretability because variable effects are not quantified in a substantively meaningful way. In this paper we describe a method for quantifying variable effects using partial dependence, which produces an estimate that can be interpreted as the effect on the response for a one unit change in the predictor, while averaging over the effects of all other variables. Most importantly, the approach avoids problems related to model misspecification and challenges to implementation in high dimensional settings encountered with other approaches (e.g., multiple linear regression). We propose and evaluate through simulation a method for constructing a point estimate of this effect size. We also propose and evaluate interval estimates based on a non-parametric bootstrap. The method is illustrated on data used for the prediction of the age of abalone.

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: A field study was carried out to determine the spatial distribution of air dose rate on grazed grassland after the earthquake on 11 March, 2011 in the Northwest Pacific of Northeastern Japan. Data on air dose rates (µSv h-1) were collected from Ichinoseki, south of Iwate Prefecture, Japan. Air dose rates were collected from each of 1 m interval of 12 ×12 m2 site (L-site). At the center of Lsite, 1.2 ×1.2 m2 site (S-site) was located. One hundred and forty four (144) equal spaced quadrats were defined in the S-site. Again, air dose rates were collected from central point of each of the quadrat. Moran’s I, a measure of autocorrelation was used to test the spatial heterogeneity of air dose rate on grazed grassland. Autocorrelation in S-site area was significantly higher than L-site area. Air dose rate did not show significant autocorrelation at any spatial lag in L-site. In S-site, air dose rate level showed significant autocorrelation in twelve of sixteen spatial lag. Autocorrelograms and Moran’s scatterplot showed that air dose rate was frequently and positively spatially correlated at distance less than 0.1 m.

Abstract: In this paper we tried to fit a predictive model for the average annual rainfall of Bangladesh through a geostatistical approach. From geostatistical point of view, we studied the spatial dependence pattern of average annual rainfall data (measured in mm) collected from 246 stations of Bangladesh. We have employed kriging or spatial interpolation for rainfall data. The data reveals a linear trend when investigated, so by fitting a linear model we tried to remove the trend and, then we used the trend-free data for further calculations. Four theoretical semivariogram models Exponential, Spherical, Gaussian and Matern were used to explain the spatial variation among the average annual rainfall. These models are chosen according to the pattern of empirical semivariogram. The prediction performance of Ordinary kriging with these four fitted models are then compared through 𝑘 fold cross-validation and it is found that Ordinary Kriging performs better when the spatial dependency in average annual rainfall of Bangladesh is modeled through Gaussian semivariogram model.

Abstract: In this paper, we introduce an extended four-parameter Fr´echet model called the exponentiated exponential Fr´echet distribution, which arises from the quantile function of the standard exponential distribution. Various of its mathematical properties are derived including the quantile function, ordinary and incomplete moments, Bonferroni and Lorenz curves, mean deviations, mean residual life, mean waiting time, generating function, Shannon entropy and order statistics. The model parameters are estimated by the method of maximum likelihood and the observed information matrix is determined. The usefulness of the new distribution is illustrated by means of three real lifetime data sets. In fact, the new model provides a better fit to these data than the Marshall-Olkin Fr´echet, exponentiated-Fr´echet and Fr´echet models.