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Publisher: Springer-Verlag (Total: 2351 journals)

 Annals of Data ScienceNumber of Followers: 12      Hybrid journal (It can contain Open Access articles) ISSN (Print) 2198-5804 - ISSN (Online) 2198-5812 Published by Springer-Verlag  [2351 journals]
• Operational Loss Data Collection: A Literature Review
• Authors: Lu Wei; Jianping Li; Xiaoqian Zhu
Pages: 313 - 337
Abstract: This paper is the first to provide a comprehensive overview of the worldwide operational loss data collection exercises (LDCEs) of internal loss, external loss, scenario analysis and business environment and internal control factors (BEICFs). Based on analyzing operational risk-related articles from 2002 to March 2017 and surveying a large amount of other information, various sources of operational risk data are classified into five types, i.e. individual banks, regulatory authorities, consortia of financial institutions, commercial vendors and researchers. Then by reviewing operational risk databases from these five data sources, we summarized and described 32 internal databases, 26 external databases, 7 scenario databases and 1 BEICFs database. We also find that compared with developing countries, developed countries have performed relatively better in operational risk LDCEs. Besides, the two subjective data elements of scenario analysis and BEICFs are less used than the two objective data elements of internal and external loss data in operational risk estimation.
PubDate: 2018-09-01
DOI: 10.1007/s40745-018-0139-2
Issue No: Vol. 5, No. 3 (2018)

• A New Approach for Improving Classification Accuracy in Predictive
Discriminant Analysis
• Authors: A. Iduseri; J. E. Osemwenkhae
Pages: 339 - 357
Abstract: The focus of a predictive discriminant analysis is to improve classification accuracy, and to obtain statistically optimal classification accuracy or hit rate is still a challenge due to the inherent variability of most real life dataset. Improving classification accuracy is usually achieved with best subset of relevant predictors obtained by using classical variable selection methods. The goal of variable selection methods is to choose the best subset (or training sample) of relevant variables that typically reduces the complexity of a model and makes it easier to interpret, improves the classification accuracy of the model and reduces the training time. However, a statistically optimal hit rate can be achieved if the training sample meets a near optimal condition by resolving any significant differences in the variances for the groups formed by the dependent variable. This paper proposes a new approach for obtaining a near optimal training sample that will produce a statistically optimal hit rate using a modified winsorization with graphical diagnostic. In application to real life data sets, the proposed new approach was able to identify and remove legitimate contaminants in one or more predictors in the training sample, thereby resolving any significant differences in the variances for the groups formed by the dependent variable. The graphical diagnostic associated with the new approach, however, provides a useful visual tool which served as an alternative graphical test for homogeneity of variances.
PubDate: 2018-09-01
DOI: 10.1007/s40745-018-0140-9
Issue No: Vol. 5, No. 3 (2018)

• Classifying Categories of SCADA Attacks in a Big Data Framework
• Authors: Krishna Madhuri Paramkusem; Ramazan S. Aygun
Pages: 359 - 386
PubDate: 2018-09-01
DOI: 10.1007/s40745-018-0141-8
Issue No: Vol. 5, No. 3 (2018)

• On Some Further Properties and Application of Weibull- R Family of
Distributions
• Authors: Indranil Ghosh; Saralees Nadarajah
Pages: 387 - 399
Abstract: In this paper, we provide some new results for the Weibull-R family of distributions (Alzaghal et al. in Int J Stat Probab 5:139–149, 2016). We derive some new structural properties of the Weibull-R family of distributions. We provide various characterizations of the family via conditional moments, some functions of order statistics and via record values.
PubDate: 2018-09-01
DOI: 10.1007/s40745-018-0142-7
Issue No: Vol. 5, No. 3 (2018)

• A Family of Generalised Beta Distributions: Properties and Applications
• Authors: Emilio Gómez-Déniz; José María Sarabia
Pages: 401 - 420
Abstract: A family of continuous distributions with bounded support, which is a generalisation of the standard beta distribution, is introduced. We study some basic properties of the new family and simulation experiments are performed to observe the behaviour of the maximum likelihood estimators. We also derive a multivariate version of the proposed distributions. Three numerical experiments were performed to determine the flexibility of the new family of distributions in comparison with other extensions of the beta distribution that have been proposed. In this respect, the new family was found to be superior.
PubDate: 2018-09-01
DOI: 10.1007/s40745-018-0143-6
Issue No: Vol. 5, No. 3 (2018)

• A New Family of Generalized Distributions Based on Alpha Power
Transformation with Application to Cancer Data
• Authors: M. Nassar; A. Alzaatreh; O. Abo-Kasem; M. Mead; M. Mansoor
Pages: 421 - 436
Abstract: In this paper, we propose a new method for generating distributions based on the idea of alpha power transformation introduced by Mahdavi and Kundu (Commun Stat Theory Methods 46(13):6543–6557, 2017). The new method can be applied to any distribution by inverting its quantile function as a function of alpha power transformation. We apply the proposed method to the Weibull distribution to obtain a three-parameter alpha power within Weibull quantile function. The new distribution possesses a very flexible density and hazard rate function shapes which are very useful in cancer research. The hazard rate function can be increasing, decreasing, bathtub or upside down bathtub shapes. We derive some general properties of the proposed distribution including moments, moment generating function, quantile and Shannon entropy. The maximum likelihood estimation method is used to estimate the parameters. We illustrate the applicability of the proposed distribution to complete and censored cancer data sets.
PubDate: 2018-09-01
DOI: 10.1007/s40745-018-0144-5
Issue No: Vol. 5, No. 3 (2018)

• Region Based Instance Document (RID) Approach Using Compression Features
• Authors: N. V. Ganapathi Raju; Someswara Rao Chinta
Pages: 437 - 451
Abstract:
Authors hip attribution is concerned with identifying authors of disputed or anonymous documents, which are potentially conspicuous in legal, criminal/civil cases, threatening letters and terroristic communications also in computer forensics. There are two basic approaches for authorship attribution one is instance based (treat each training text individually) and the other is profile based (treat each training text cumulatively). Both of these methods have their own advantages and disadvantages. The present paper proposes a new region based document model for authorship identification, to address the dimensionality problem of instance based approaches and scalability problem of profile based approaches. The proposed model concatenates a set of individual ‘n’ instance documents of the author as a single region based instance document (RID). On the RID compression based similarity distance method is used. The compression based methods requires no pre-processing and easy to apply. This paper uses Gzip compression algorithm with two compression based similarity measures NCD, CDM. The proposed compression model is character based and it can automatically capture easily non word features such as word stems, punctuations etc. The only disadvantage of compression models is complexity is high. The proposed RID approach addresses this issue by reducing the repeated words in the document. The present approach is experimented on English editorial columns. We achieved approximately 98% of accuracy in identifying the author.
PubDate: 2018-09-01
DOI: 10.1007/s40745-018-0145-4
Issue No: Vol. 5, No. 3 (2018)

• Development of Optimal ANN Model to Estimate the Thermal Performance of
Roughened Solar Air Heater Using Two different Learning Algorithms
Pages: 453 - 467
Abstract: In the present study, artificial neural network (ANN) model has been developed with two different training algorithms to predict the thermal efficiency of wire rib roughened solar air heater. Total 50 sets of data have been taken from experiments with three different types of absorber plate. The experimental data and calculated values of collector efficiency were used to develop ANN model. Scaled conjugate gradient (SCG) and Levenberg–Marquardt (LM) learning algorithms were used. It has been found that TRAINLM with 6 neurons and TRAINSCG with 7 neurons is optimal model on the basis of statistical error analysis. The performance of both the models have been compared with actual data and found that TRAINLM performs better than TRAINSCG. The value of coefficient of determination $$(\hbox {R}^{2})$$ for LM-6 is 0.99882 which gives the satisfactory performance. Learning algorithm with LM based proposed MLP ANN model seems more reliable for predicting performance of solar air heater.
PubDate: 2018-09-01
DOI: 10.1007/s40745-018-0146-3
Issue No: Vol. 5, No. 3 (2018)

• $$\ell _1$$ ℓ 1 -Norm Based Central Point Analysis for Asymmetric Radial
Data
• Authors: Qi An; Shu-Cherng Fang; Tiantian Nie; Shan Jiang
Pages: 469 - 486
Abstract: Multivariate asymmetric radial data clouds with irregularly positioned “spokes” and “clutters” are commonly seen in real life applications. In identifying the spoke directions of such data, a key initial step is to locate a central point from which each spoke extends and diverges. In this technical note, we propose a novel method that features a preselection procedure to screen out candidate points that have sufficiently many data points in the vicinity and identifies the central point by solving an $$\ell _1$$ -norm constrained discrete optimization program. Extensive numerical experiments show that the proposed method is capable of providing central points with superior accuracy and robustness compared with other known methods and is computationally efficient for implementation.
PubDate: 2018-09-01
DOI: 10.1007/s40745-018-0147-2
Issue No: Vol. 5, No. 3 (2018)

• Enhancing Situation Awareness Using Semantic Web Technologies and Complex
Event Processing
• Authors: Havva Alizadeh Noughabi; Mohsen Kahani; Alireza Shakibamanesh
Pages: 487 - 496
Abstract: Data fusion techniques combine raw data of multiple sources and collect associated data to achieve more specific inferences than what could be attained with a single source. Situational awareness is one of the levels of the JDL, a matured information fusion model. The aim of situational awareness is to understand the developing relationships of interests between entities within a specific time and space. The present research shows how semantic web technologies, i.e. ontology and semantic reasoner, can be used to describe situations and increase awareness of the situation. As the situation awareness level receives data streams from numerous distributed sources, it is necessary to manage data streams by applying data stream processor engines such as Esper. In addition, in this research, complex event processing, a technique for achieving related situational in real-time, has been used, whose main aim is to generate actionable abstractions from event streams, automatically. The proposed approach combines Complex Event Processing and semantic web technologies to achieve better situational awareness. To show the functionality of the proposed approach in practice, some simple examples are discussed.
PubDate: 2018-09-01
DOI: 10.1007/s40745-018-0148-1
Issue No: Vol. 5, No. 3 (2018)

• Modelling Under-Five Mortality among Hospitalized Pneumonia Patients in
Hawassa City, Ethiopia: A Cross-Classified Multilevel Analysis
• Authors: Tariku Tessema
Pages: 111 - 132
Abstract: Community acquired pneumonia refers to pneumonia acquired outside of hospitals or extended health facilities and it is a leading infectious disease. This study aims to model mortality of hospitalized under-5 year child pneumonia patients and investigate potential risk factors associated with child mortality due to pneumonia. The study was a retrospective study on 305 sampled under-five hospitalized patients of community acquired pneumonia. A cross-classified multilevel logistic regression was employed with resident and hospital classified at the second level. Bayesian estimation method was applied in which the posterior distribution was simulated via Markov Chain Monte Carlo. The variability attributable to hospital was found to be larger than variability attributable to residence. The odds of dying from the community acquired pneumonia was higher among patients who were; diagnosed in spring season, complicated with malaria, AGE and AFI, in a neonatal age group, diagnosed late (more than a week). The risk of mortality was also found high for lower nurse: patient and physician: patients’ ratios.
PubDate: 2018-06-01
DOI: 10.1007/s40745-017-0121-4
Issue No: Vol. 5, No. 2 (2018)

• Parallel String Matching with Linear Array, Butterfly and Divide and
Conquer Models
• Authors: S. Viswanadha Raju; K. K. V. V. S. Reddy; Chinta Someswara Rao
Pages: 181 - 207
Abstract: String Matching is a technique of searching a pattern in a text. It is the basic concept to extract the fruitful information from large volume of text, which is used in different applications like text processing, information retrieval, text mining, pattern recognition, DNA sequencing and data cleaning etc., . Though it is stated some of the simple mechanisms perform very well in practice, plenty of research has been published on the subject and research is still active in this area and there are ample opportunities to develop new techniques. For this purpose, this paper has proposed linear array based string matching, string matching with butterfly model and string matching with divide and conquer models for sequential and parallel environments. To assess the efficiency of the proposed models, the genome sequences of different sizes (10–100 Mb) are taken as input data set. The experimental results have shown that the proposed string matching algorithms performs very well compared to those of Brute force, KMP and Boyer moore string matching algorithms.
PubDate: 2018-06-01
DOI: 10.1007/s40745-017-0124-1
Issue No: Vol. 5, No. 2 (2018)

• Evaluation and Comparison of Estimators in the Gompertz Distribution
• Authors: Sanku Dey; Tanmay Kayal; Yogesh Mani Tripathi
Pages: 235 - 258
Abstract: This article addresses the different methods of estimation of the probability density function and the cumulative distribution function for the Gompertz distribution. Following estimation methods are considered: maximum likelihood estimators, uniformly minimum variance unbiased estimators, least squares estimators, weighted least square estimators, percentile estimators, maximum product of spacings estimators, Cramér–von-Mises estimators, Anderson–Darling estimators. Monte Carlo simulations are performed to compare the behavior of the proposed methods of estimation for different sample sizes. Finally, one real data set and one simulated data set are analyzed for illustrative purposes.
PubDate: 2018-06-01
DOI: 10.1007/s40745-017-0126-z
Issue No: Vol. 5, No. 2 (2018)

• Equalization and Carrier Frequency Offset Compensation for Underwater
Acoustic OFDM Systems
• Authors: K. Ramadan; M. I. Dessouky; S. Elagooz; M. Elkordy; F. E. Abd El-Samie
Pages: 259 - 272
Abstract: Due to noise enhancement, conventional Zero Forcing (ZF) equalizers are not suitable for wireless Underwater Acoustic (UWA) Orthogonal Frequency Division Multiplexing (OFDM) communication systems. Furthermore, these systems suffer from increasing complexity due to the large number of subcarriers, especially in Multiple-Input Multiple-Output (MIMO) systems. On the other hand, the Minimum Mean Square Error equalizer suffers from high complexity. This type of equalizers needs an estimation of the operating Signal-to-Noise Ratio to work properly. In this paper, we propose a Joint Low-Complexity Regularized ZF equalizer for MIMO UWA-OFDM systems to cope with these problems. The main objective of the proposed equalizer is to enhance the system performance with a lower complexity by performing equalization in two steps. The co-channel interference can be mitigated in the first step. A regularization term is added in the second step to avoid the noise enhancement. Simulation results show that the proposed equalization scheme has the ability to enhance the UWA system performance with low complexity.
PubDate: 2018-06-01
DOI: 10.1007/s40745-017-0127-y
Issue No: Vol. 5, No. 2 (2018)

• Cardiopulmonary Function Monitoring Based on MEWMA Control Chart
• Authors: Hongxia Zhang; Liu Liu; Jin Yue; Xin Lai
Pages: 293 - 299
Abstract: According to the characteristics of parameters of cardiopulmonary function diversity and change slowly in pathology, we apply the multivariate exponentially weighted moving average (MEWMA) control chart to monitor the state of lungs. This paper aimed at five indicators of cardiopulmonary function, using principal component test to diagnose whether it is from the multivariate normal distribution, Clearing the relationship model of control line and weight coefficient of MEWMA control graph, and drawing the control diagram for monitoring. The process stay in control state before 103 observations, however, beyond the control limit from the 104 observation statistics and give an alarm. This means that there is a problem with the cardiopulmonary starting on the 103rd sample. Control chart has a good warning function because it can raise the alarm before cardiopulmonary function has a big problem. Using MEWMA control chart for monitoring can reduce the cost of medical examination and frequency, it can improve the hospital resource utilization rate and confirm the case. Thus we can avoid missing the best treatment time.
PubDate: 2018-06-01
DOI: 10.1007/s40745-018-0137-4
Issue No: Vol. 5, No. 2 (2018)

• Analysis on Housing Affordability of Urban Residents in Mainland China
Based on Multiple Indexes: Taking 35 Cities as Examples
• Authors: Yun Si Li; Ai Hua Li; Zhi Feng Wang; Qiang Wu
Abstract: Over the last 10 years, the soaring housing prices have raised concerns over ‘affordability’ in Chinese housing market, although it is still not enshrined in agreed standards, partly because of different opinions about how it should be measured. To overcome the inadequacy of a single index, we examine the housing affordability of 35 large and medium cities in China from 2009 to 2016 using price-to-income ratio (PIR), monthly payment-income ratio (MIR) and the residual income approach (RI). With consideration of the characteristics of China’s real estate market, we have re-discussed the reasonable range of the indexes. The comparison of single index between cities shows significant periodicity and multi-index clustering analysis reveals regional characteristics, which help us to further the understanding of housing affordability. In the end, policy recommendations on reforming Chinese urban housing system are suggested according to the differences and changing laws of housing affordability among cities.
PubDate: 2018-06-19
DOI: 10.1007/s40745-018-0168-x

• The Zubair-G Family of Distributions: Properties and Applications
Abstract: In this article, a new method is suggested to expand a family of life distributions by adding an additional parameter. The new proposal may be named as the Zubair-G family of distributions. For this family, general expressions for some mathematical properties are derived. The maximum product spacing, ordinary least square and maximum likelihood methods are discussed to estimate the model parameters. A three-parameter special sub-model of the proposed family, called the Zubair–Weibull distribution is considered in detail. Its density function can be symmetrical, left-skewed, right-skewed, and has increasing, decreasing, bathtub and upside-down bathtub shaped failure rates. To illustrate the importance of the proposed family over the other well-known methods, two applications to real data sets are analyzed.
PubDate: 2018-06-18
DOI: 10.1007/s40745-018-0169-9

• On Modified Extended Exponential Power Life Testing Distribution:
Development, Properties, Characterizations and Application
• Authors: Fiaz Ahmad Bhatti; G. G. Hamedani; Seyed Morteza Najibi; Munir Ahmad
Abstract: In this paper, a flexible modified extended exponential power life testing (MEEPLT) distribution is proposed. The MEEPLT distribution has increasing, decreasing and bathtub hazard rate function. The MEEPLT density is arc, left skewed, right-skewed and symmetrical shaped. The MEEPLT distribution is developed on the basis of the generalized Pearson differential equation. Some structural and mathematical properties including descriptive measures on the basis of quantiles, moments, order statistics and reliability measures are theoretically established. Characterizations of MEEPLT distribution are also studied via different techniques. Parameters of the MEEPLT distribution are estimated using maximum likelihood method. The simulation study for performance of the MLEs of the MEEPLT distribution is carried out. Goodness of fit of this distribution through different methods is studied.
PubDate: 2018-06-16
DOI: 10.1007/s40745-018-0167-y

PubDate: 2018-06-06
DOI: 10.1007/s40745-018-0164-1

• The Exponentiated Generalized Marshall–Olkin Family of Distribution: Its
Properties and Applications
• Authors: Laba Handique; Subrata Chakraborty; Thiago A. N. de Andrade
Abstract: A new generator of continuous distributions called Exponentiated Generalized Marshall–Olkin-G family with three additional parameters is proposed. This family of distribution contains several known distributions as sub models. The probability density function and cumulative distribution function are expressed as infinite mixture of the Marshall–Olkin distribution. Important properties like quantile function, order statistics, moment generating function, probability weighted moments, entropy and shapes are investigated. The maximum likelihood method to estimate model parameters is presented. A simulation result to assess the performance of the maximum likelihood estimation is briefly discussed. A distribution from this family is compared with two sub models and some recently introduced lifetime models by considering three real life data fitting applications.
PubDate: 2018-06-05
DOI: 10.1007/s40745-018-0166-z

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