Modeling Framework For Data Research Articles

Single Marker Family-Based Association Analysis Not Conditional on Parental Information.

Family-based association analysis unconditional on parental genotypes models the effects of observed genotypes. This approach has been shown to have greater power than conditional methods. In this chapter, we review popular association analysis methods accounting for familial correlations: the marginal model using generalized estimating equations (GEE), the mixed model with a polygenic random component, and genome-wide association analyses. The marginal approach does not explicitly model familial correlations but uses the information to improve the efficiency of parameter estimates. This model, using GEE, is useful when the correlation structure is not of interest; the correlations are treated as nuisance parameters. In the mixed model, familial correlations are modeled as random effects, e.g., the polygenic inheritance model accounts for correlations originating from shared genomic components within a family. These unconditional methods provide a flexible modeling framework for general pedigree data to accommodate traits with various distributions and many types of covariate effects. Genome-wide association studies usually test more than 10,000 SNPs and thus traditional statistical methods accounting for the familial correlations often suffer from a computational burden. Multiple approaches that have been recently proposed to avoid this computational issue are reviewed. The single-marker analysis procedures are demonstrated using the R package gee and the ASSOC program in the S.A.G.E. package, including how to prepare input data, conduct the analysis, and interpret the output. ASSOC allows models to include random components of additional familial correlations that may be not sufficiently explained by a polygenic effect and addresses nonnormality of response variables by transformation methods. With its ease of use, ASSOC provides a useful tool for association analysis of large pedigree data.

Model selection for discrete regular vine copulas

Discrete vine copulas provide a flexible modeling framework for high-dimensional data and have significant computational advantages over competing methods. A vine-based multivariate probability mass function is constructed from bivariate copula building blocks and univariate marginal distributions. However, even for a moderate number of variables, the number of alternative vine decompositions is very large and additionally there is a large set of candidate bivariate copula families that can be used as building blocks in any given decomposition. Together, these two issues ensure that it is infeasible to evaluate all possible vine copula models. Instead, two greedy algorithms for automatically selecting vine structures and component pair-copula building blocks are introduced. The algorithms are tested in a simulation study that is itself driven by real world data from online retail. Both algorithms select vines that provide accurate estimates of the joint probabilities. Using three different f-divergences as criteria, the proposed algorithms outperform a Gaussian copula benchmark, especially for data with high dependence. Finally, the selection algorithm is applied to data from the General Social Survey and outperforms a Gaussian copula benchmark using both in-sample and out-of-sample criteria.

A simplified estimation procedure based on the EM algorithm for the power series cure rate model

ABSTRACTThe family of power series cure rate models provides a flexible modeling framework for survival data of populations with a cure fraction. In this work, we present a simplified estimation procedure for the maximum likelihood (ML) approach. ML estimates are obtained via the expectation-maximization (EM) algorithm where the expectation step involves computation of the expected number of concurrent causes for each individual. It has the big advantage that the maximization step can be decomposed into separate maximizations of two lower-dimensional functions of the regression and survival distribution parameters, respectively. Two simulation studies are performed: the first to investigate the accuracy of the estimation procedure for different numbers of covariates and the second to compare our proposal with the direct maximization of the observed log-likelihood function. Finally, we illustrate the technique for parameter estimation on a dataset of survival times for patients with malignant melanoma.

On discovering co-location patterns in datasets: a case study of pollutants and child cancers

We intend to identify relationships between cancer cases and pollutant emissions and attempt to understand whether cancer in children is typically located together with some specific chemical combinations or is independent. Co-location pattern analysis seems to be the appropriate investigation to perform. Co-location mining is one of the tasks of spatial data mining which focuses on the detection of co-location patterns, the sets of spatial features frequently located in close proximity of each other. Most previous works are based on transaction-free apriori-like algorithms which are dependent on user-defined thresholds and are designed for boolean data points. Due to the absence of a clear notion of transactions, it is nontrivial to use association rule mining techniques to tackle the co-location mining problem. The approach we propose is based on a grid "transactionization" of the geographic space and is designed to mine datasets with extended spatial objects. Uncertainty of the feature presence in transactions is taken into account in our model. The statistical test is used instead of global thresholds to detect significant co-location patterns and rules. We evaluate our approach on synthetic and real datasets. This approach can be used by researchers looking for spatial associations between environmental and health factors. In addition, we explain the data modelling framework which is used on real datasets of pollutants (PRTR/NPRI) and childhood cancer cases.

Analyzing Th17 cell differentiation dynamics using a novel integrative modeling framework for time-course RNA sequencing data

BackgroundThe differentiation of naive CD 4+ helper T (Th) cells into effector Th17 cells is steered by extracellular cytokines that activate and control the lineage specific transcriptional program. While the inducing cytokine signals and core transcription factors driving the differentiation towards Th17 lineage are well known, detailed mechanistic interactions between the key components are poorly understood.ResultsWe develop an integrative modeling framework which combines RNA sequencing data with mathematical modeling and enables us to construct a mechanistic model for the core Th17 regulatory network in a data-driven manner.ConclusionsOur results show significant evidence, for instance, for inhibitory mechanisms between the transcription factors and reveal a previously unknown dependency between the dosage of the inducing cytokine TGF β and the expression of the master regulator of competing (induced) regulatory T cell lineage. Further, our experimental validation approves this dependency in Th17 polarizing conditions.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-015-0223-6) contains supplementary material, which is available to authorized users.

A novel modeling framework for ordinal data defined by collapsed counts.

Adolescent alcohol use is a serious public health concern. Despite advances in the theoretical conceptualization of pathways to alcohol use, researchers are limited by the statistical techniques currently available. Researchers often fit linear models and restrictive categorical models (e.g., proportional odds models) to ordinal data with many response categories defined by collapsed count data (0 drinking days, 1-2 days, 3-6 days, etc.). Consequently, existing models ignore the underlying count process, resulting in disjoint between the construct of interest and the models being fitted. Our proposed ordinal modeling approach overcomes this limitation by explicitly linking ordinal responses to a suitable underlying count distribution. In doing so, researchers can use maximum likelihood estimation to fit count models to ordinal data as if they had directly observed the underlying discrete counts. The usefulness of our ordinal negative binomial and ordinal zero-inflated negative binomial models is verified by simulation studies. We also demonstrate our approach using real empirical data from the 2010 National Survey of Drug Use and Health. Results show the benefit of the proposed ordinal modeling framework compared with existing methods.

Variational learning of hierarchical infinite generalized Dirichlet mixture models and applications

Data clustering is a fundamental unsupervised learning task in several domains such as data mining, computer vision, information retrieval, and pattern recognition. In this paper, we propose and analyze a new clustering approach based on both hierarchical Dirichlet processes and the generalized Dirichlet distribution, which leads to an interesting statistical framework for data analysis and modelling. Our approach can be viewed as a hierarchical extension of the infinite generalized Dirichlet mixture model previously proposed in Bouguila and Ziou (IEEE Trans Neural Netw 21(1):107---122, 2010). The proposed clustering approach tackles the problem of modelling grouped data where observations are organized into groups that we allow to remain statistically linked by sharing mixture components. The resulting clustering model is learned using a principled variational Bayes inference-based algorithm that we have developed. Extensive experiments and simulations, based on two challenging applications namely images categorization and web service intrusion detection, demonstrate our model usefulness and merits.

Investigating the contribution of Demand Responsive Transport to a sustainable local public transport system

Various studies have advocated the potential for Demand Responsive Transport (DRT) services to deliver sustainable local public transport. This paper investigates the sustainability credentials of DRT services using evidence from UK-based research. More specifically, six potential DRT market niches were identified, including those which offer potential commercial opportunities (e.g. airport surface access) and those that meet social needs (e.g. non-emergency hospital trips). Mode share of these DRT services, against car or bus travel, was simulated from mixed logit models within a panel data modelling framework estimated from survey data. The survey was conducted of over 400 respondents in urban (Rochdale, Manchester) and rural (Melton Mowbray, Leicestershire) areas.Experience shows that it is particularly difficult to make DRT services financially viable. Of the DRT services investigated, those targeting airline or train passengers offer potential. However, they are in direct competition with the car, and so their success depends on the cost and availability of parking spaces. Some of the DRT schemes explored meet social needs, such as to access shopping facilities or hospitals, but they face cost challenges. In addition, institutional barriers for new DRT schemes need to be overcome in order to develop a sustainable local public transport system.

Spiking neural network methodology for modelling, classification and understanding of EEG spatio-temporal data measuring cognitive processes

The paper offers a new methodology for modelling, recognition and understanding of electroencephalography (EEG) spatio-temporal data measuring complex cognitive brain processes during mental tasks. The key element is that mental tasks are performed through complex spatio-temporal brain processes and they can be better understood only if we model properly the spatio-/spectro temporal data that measures these processes. The proposed methodology is based on a recently proposed novel spiking neural network architecture, called NeuCube as a general framework for spatio-temporal brain data modelling. The methodology is demonstrated on benchmark cognitive EEG data. The new approach leads to a faster data processing, improved accuracy of the EEG data classification and improved understanding of this data and the cognitive processes that generated it. The paper concluded that the new methodology is worth exploring further on other spatio-temporal data, measuring complex cognitive brain processes, aiming at using this method for the development of the next generation of brain–computer interfaces and systems for early diagnosis of degenerative brain disease, such as Alzheimer’s Disease (AD), and for personalised neuro-rehabilitation systems.

Peach 퍼징을 위한 파일 분석 데이터 자동 생성 모델

소프트웨어가 산업 및 사회전반으로 저변을 확대함에 따라 소프트웨어의 취약점으로 인한 위험이 증가하고 있으며, 소프트웨어의 취약점을 악용하는 사례도 빈번하게 나타나고 있다. 스마트 퍼징은 이러한 소프트웨어 취약점을 발견하기 위한 자동화된 방법이다. 그러나 스마트 퍼징을 위해서는 우선 퍼징을 수행하고자 하는 대상 소프트웨어에 대한 데이터 모델을 생성해야하며, 데이터 파일 및 소프트웨어 자체에 대한 분석이 필요하기 때문에 많은 자원이 소모된다. 따라서 효율적인 스마트 퍼징을 위해서 데이터 모델을 자동으로 생성하기 위한 방법이 필요하다. 본 논문에서는 데이터 입력 파일에 대한 분석을 통해서 스마트 퍼징을 위한 데이터 모델을 자동으로 생성하기 위한 프레임워크를 제안하고 이를 구현함으로서 소프트웨어 취약점 발견에 도움을 줄 것으로 기대한다. The rapid expansion of the software industry has brought a serious security threat and vulnerability. Many softwares are constantly attacked by exploit codes using security vulnerabilities. Smart fuzzing is automated method to find software vulnerabilities. However, Many resources are consumed in fuzzing, because the fuzzing needs to create data model for target software and to analyze a data file and software binary. Therefore, The automated method for efficient smart fuzzing is needed to develop the automated data model. In this paper, through analysing the input file format and optimizing the data structure, we propose an efficient data modeling framework for smart fuzzing and implement the framework for detect software vulnerabilities.

Robust Linear Static Panel Data Models Using Ε-Contamination

The paper develops a general Bayesian framework for robust linear static panel data models using ?-contamination. A two-step approach is employed to derive the conditional type-II maximum likelihood (ML-II) posterior distribution of the coefficients and individual effects. The ML-II posterior densities are weighted averages of the Bayes estimator under a base prior and the data-dependent empirical Bayes estimator. Two-stage and three stage hierarchy estimators are developed and their finite sample performance is investigated through a series of Monte Carlo experiments. These include standard random effects as well as Mundlak-type, Chamberlain-type and Hausman-Taylor-type models. The simulation results underscore the relatively good performance of the three-stage hierarchy estimator. Within a single theoretical framework, our Bayesian approach encompasses a variety of specifications while conventional methods require separate estimators for each case. We illustrate the performance of our estimator relative to classic panel estimators using data on earnings and crime.

Interaction between GIS and hydrologic model: A preliminary approach using ArcHydro Framework Data Model

In different regions of Brazil, population growth and economic development can degrade water quality, compromising watershed health and human supply. Because of its ability to combine spatial and temporal data in the same environment and to create water resources management (WRM) models, the Geographical Information System (GIS) is a powerful tool for managing water resources, preventing floods and estimating water supply. This paper discusses the integration between GIS and hydrological models and presents a case study relating to the upper section of the Paraiba do Sul Basin (Sao Paulo State portion), situated in the Southeast of Brazil. The case study presented in this paper has a database suitable for the basin’s dimensions, including digitized topographic maps at a 50,000 scale. From an ArcGIS ® /ArcHydro Framework Data Model, a geometric network was created to produce different raster products. This first grid derived from the digital elevation model grid (DEM) is the flow direction map followed by flow accumulation, stream and catchment maps. The next steps in this research are to include the different multipurpose reservoirs situated along the Paraiba do Sul River and to incorporate rainfall time series data in ArcHydro to build a hydrologic data model within a GIS environment in order to produce a comprehensive spatial-temporal model.

Dataspace and its application in digital libraries

Purpose – This paper aims to introduce dataspace into digital libraries in order to address its emerging data management challenges which stem from cross-domain, heterogeneous, and uncertain nature of data resources, based on looking into the fundamental principles, significant features, and research directions of dataspace. Design/methodology/approach – This research mainly employs three types of research methodologies: a literature study was conducted for revealing the fundamental principles, analyzing the significant features, and discussing the new research topics; knowledge engineering methodology is used to design the data model; software engineering methodology is applied to develop the reference framework and the digital library. Findings – This paper for the first time proposes a motion to build a Dataspace Support Digital Library (DSDL), and provides its data model, data management policies and a reference framework. Further, its implementation is described and some implications learned from the case study are also discussed. Practical implications – Introducing dataspace technologies into developing a digital library frees the developer to just focus on solving business challenges, rather than addressing non-business related, data management level tasks. In addition, the data model and the reference framework presented in this paper lay foundations for constructing Dataspace Support Digital Libraries. Originality/value – This is the first paper to introduce dataspaces into the design of digital libraries and is also the first paper to propose a novel data model, data management and reference framework for Dataspace Support Digital Libraries.

Grammatical Inference for Modeling Mobility Patterns in Networks

Modeling of the mobility patterns arising in computer networks requires a compact and faithful representation of the mobility data collected from observations and measurements of the relevant network applications. This data can range from the information on the mobility of the agents that are being monitored by a wireless network to mobility information of nodes in mobile network applications. In this paper, we examine the use of probabilistic context-free grammars as the modeling framework for such data. We present a fast algorithm for deriving a concise probabilistic context-free grammar from the given training data. The algorithm uses an evaluation metric based on Bayesian formula for maximizing grammar a posteriori probability given the training data. We describe the application of this algorithm in two mobility modeling domains: 1) recognizing mobility patterns of monitored agents in different event data sets collected by sensor networks, and 2) modeling and generating node movements in mobile networks. We also discuss the model's performance in simulations utilizing both synthetic and real-world mobility traces.

An Intelligent Decision Support System (IDSS) for Nutrition Therapy

In this paper, the authors have presented an expert system to support decision makers in nutrition therapy planning. This system is an extended version of a fuzzy decision support system for nutrition therapy. The presented expert system is equipped with an updated knowledge base component by using a set of rules. Also in order to deal with vagueness and uncertainty, fuzzy set theory could provide a suitable framework for data management, modelling and decision support. Therefore, fuzzy rules empower this system to be implemented more realistically. In addition, for developing knowledge management component, artificial neural network (ANN) is applied to survey the input data and information in the long term. The integration of ANN with the expert system provides the possibility for a set of novel rules to be generated and consequently adds new knowledge to the system.

A relational framework and case shells of a new type

This paper describes a CASE shell of a new type, namely, the instrumental system SWS constructed in accordance with a framework data model. A scheme of the instrumental system is proposed. The list of main user functions of the SWS shows that the typification and formalization of requests to a database with a well-formalized data model and unification and minimization of the interface allow modelling the totality of user requests. The results of a numerical experiment on the real-time formation of OLAP data elements are presented. The real-time approach is shown to be most advanced.

Rethinking the import-productivity nexus for Italian manufacturing

We provide evidence on the firm level productivity effects of imports of intermediates. By exploiting a large panel of Italian manufacturing firms, we are able to separately explore the role of importing from high and low income countries. Importing does not permanently affect the firm productivity growth. This finding holds both when we test for the import entry by means of Propensity Score Matching techniques and when we analyse the import intensity within a dynamic panel data model framework. On the contrary, we confirm the existence of self-selection into importing. Also, our evidence supports the learning-by-exporting effects in Italian manufacturing and we prove that this result is robust to the control of firm import activity.

Step-Based Data Sharing and Exchange in One-of-a-Kind Product Collaborative Design for Cloud Manufacturing

With the trend for global collaboration, there is a need for collaborative design between geographically distributed teams and companies. In particular, this need is inevitable in the companies doing their business based on one-of-a-kind production (OKP). One important problem is the lack of interoperability and compatibility of data between different CAx systems. This problem is further highlighted in data exchange in cloud manufacturing. To the best of authors' knowledge, current studies have limitations in achieving the interoperability and compatibility of data. In this paper, a STEP-based data model is proposed to represent OKP product data/knowledge, which contains four categories of product knowledge (i.e., customer, product, manufacturing, and resource resp.). A STEP-based data modelling approach is proposed to describe each category of knowledge separately and then connect them to form the final integrated model. Compared with most current product models, this model includes the more complete product data/knowledge involved in OKP product development (OKPPD), and thus it can provide more adequate knowledge support for OKPPD activities. Based on the proposed STEP-based data model, a product data exchange and sharing (DES) framework is proposed and developed to enable DES in collaborative OKPPD in the cloud manufacturing environment. Case studies were carried out to validate the proposed data model and DES framework.

On online high-dimensional spherical data clustering and feature selection

Motivated by the high demand to construct compact and accurate statistical models that are automatically adjustable to dynamic changes, in this paper, we propose an online probabilistic framework for high-dimensional spherical data modeling. The proposed framework allows simultaneous clustering and feature selection in online settings using finite mixtures of von Mises distributions (movM). The unsupervised learning of the resulting model is approached using Expectation Maximization (EM) for parameter estimation along with minimum message length (MML) to determine the optimal number of mixture components. The gradient stochastic descent approach is considered for incremental updating of model parameters, also. Through empirical experiments, we demonstrate the merits of the proposed learning framework on diverse high dimensional datasets and challenging applications.

Estimating potential yield of wheat production in China based on cross-scale data-model fusion

The response of the agro-ecological system to the environment includes the response of individual crop's physiological process and the adaption of the crop community to the environment and its change. Observation and simulation at the single scale level cannot fully explain the above process. It is necessary to develop cross-scale agro-ecological models and study the interaction of agro-ecological processes across different scales. Two typical agro-ecological models (DSSAT and AEZ) are employed in this study and a framework for effective cross-scale data-model and model-model fusion is proposed and illustrated based on the application of DSSAT and AEZ models. The site-specific crop model is up-scaled by using this data-model fusion method, observed data from 36 different agricultural observation stations nationwide and historical weather stations observations (1962-1990) are employed and average crop productivity are estimated. Comparison of the estimation results shows the major consistency across the two models and also indicates specific directions for further improvement. This would help the future efforts in cross-scale crop model fusion.