However, in the Big Data era, the large sample size enables us to better understand heterogeneity, shedding light toward studies such as exploring the association between certain covariates (e.g. There are different types of synchrony and it is important that data is in sync otherwise this can impact the entire process. According to surveys being conducted many companies are opening up to using big data analytics in their daily functioning. Accuracy in managing big data will lead to more confident decision making. Issues with data capture, cleaning, and storage Besides PCA and RP, there are many other dimension-reduction methods, including latent semantic indexing (LSI) [112], discrete cosine transform [113] and CUR decomposition [114]. The challenge of getting data into the big data platform: Every company is different and has different amounts of data to deal with. Moreover, the theory of RP depends on the high dimensionality feature of Big Data. More specifically, let us consider the high-dimensional linear regression model (, \begin{eqnarray} Why do we need dimension reduction? Understanding this is extremely important for companies as only choosing the right tool and core data magnet landscape is the fine line between success and failure. \end{equation}, There are two main ideas of sure independent screening: (i) it uses the marginal contribution of a covariate to probe its importance in the joint model; and (ii) instead of selecting the most important variables, it aims at removing variables that are not important. \end{equation*}, \begin{eqnarray} While data is important, even more, important is the process through which companies can gain insights with their help. 2. \widehat{S} = \lbrace j: |\widehat{\beta }^{M}_j| \ge \delta \rbrace \#{\rm A} =5, \#{\rm T} =4, \#{\rm G} =5, \#{\rm C} =6. When big data analytics challenges are addressed in a proper manner, the success rate of implementing big data solutions automatically increases. Velocity — One of the major challenges is handling the flow of information as it is collected. Plots of the median errors in preserving the distances between pairs of data points versus the reduced dimension k in large-scale microarray data. Big Data: The Way Ahead By augmenting the existing data storage and providing access to end users, big data analytics needs to be comprehensive and insightful. \end{equation*}, To handle the noise-accumulation issue, we assume that the model parameter, \begin{equation} \end{eqnarray}, Take high-dimensional classification for instance. Complex data challenge: due to the fact that Big Data are in general aggregated from multiple sources, they sometime exhibit heavy tail behaviors with nontrivial tail dependence. ) may not be concave, the authors of [100] proposed an approximate regularization path following algorithm for solving the optimization problem in (9). This result guarantees that RTR can be sufficiently close to the identity matrix. \end{eqnarray}, Consider the problem of estimating the coefficient vector, \begin{equation} The authors thank the associate editor and referees for helpful comments. Challenges for Success in Big Data and Analytics When considering your Big Data projects and architecture, be mindful that there are a number of challenges that need to be addressed for you to be successful in Big Data and analytics. Implementation of Hadoop infrastructure. Variety — Handling and managing different types of data, their formats and sources is a big challenge. In classical settings where the sample size is small or moderate, data points from small subpopulations are generally categorized as ‘outliers’, and it is hard to systematically model them due to insufficient observations. Big Data Analytics Challenges. As big data makes its way into companies and brands around the world, addressing these challenges is extremely important. In addition, the size and volume of data is increasing every single day, making it important to address the manner in which big data is addressed every day. \widehat{R} = \max _{|S|=4}\max _{\lbrace \beta _j\rbrace _{j=1}^4} \left|\widehat{\mathrm{Corr}}\left (X_{1}, \sum _{j\in S}\beta _{j}X_{j} \right )\right|. Accordingly, the popularity of this dimension reduction procedure indicates a new understanding of Big Data. Securing Big Data. Theoretical justifications of RP are based on two results. This means that companies must always invest in the right resources, be it technology or expertise so that they can ensure that their goals and objectives are objectively met in a sustained manner. In practice, the authors of [110] showed that in high dimensions we do not need to enforce the matrix to be orthogonal. Successful implementation of big data analytics, therefore, requires a combination of skills, people and processes that can work in perfect synchronization with each other. In fact, new models are being developed within each NoSQL categories, that help companies reach goals. To protect the rights of the author(s) and publisher we inform you that this PDF is an uncorrected proof for … Random projection (RP) [, \begin{equation*} Security challenges of big data are quite a vast issue that deserves a whole other article dedicated to the topic. While companies will be skeptical about implementing business analytical and big data within the organization, once they understand the immense potential associated with it, they will easily be more open and adaptable to the entire big data analytical process. This means that many data tool experts do not have the required knowledge about the practical aspects of data modeling, data architecture, and data integration. Statistically, they show that any local solution obtained by the algorithm attains the oracle properties with the optimal rates of convergence. On the other hand, the massive sample size and high dimensionality of Big Data introduce unique computational and statistical challenges, including … Issue Over the Value of Big Data. Another thing to keep in mind is that many experts in the field of big data have gained their experience through tool implementation and its use as a programming model as opposed to data management aspects. \end{equation}, In high dimensions, even for a model as simple as (, \begin{eqnarray} The ADHD-200 consortium: a model to advance the translational potential of neuroimaging in clinical neuroscience, Detecting outliers in high-dimensional neuroimaging datasets with robust covariance estimators, Transition matrix estimation in high dimensional time series, Forecasting using principal components from a large number of predictors, Determining the number of factors in approximate factor models, Inferential theory for factor models of large dimensions, The generalized dynamic factor model: one-sided estimation and forecasting, High dimensional covariance matrix estimation using a factor model, Covariance regularization by thresholding, Adaptive thresholding for sparse covariance matrix estimation, Noisy matrix decomposition via convex relaxation: optimal rates in high dimensions, High-dimensional semiparametric Gaussian copula graphical models, Regularized rank-based estimation of high-dimensional nonparanormal graphical models, Large covariance estimation by thresholding principal orthogonal complements, Twitter catches the flu: detecting influenza epidemics using twitter, Variable selection in finite mixture of regression models, Phase transition in limiting distributions of coherence of high-dimensional random matrices, ArrayExpress—a public repository for microarray gene expression data at the EBI, Discoidin domain receptor tyrosine kinases: new players in cancer progression, A new look at the statistical model identification, Risk bounds for model selection via penalization, Ideal spatial adaptation by wavelet shrinkage, Longitudinal data analysis using generalized linear models, A direct estimation approach to sparse linear discriminant analysis, Simultaneous analysis of lasso and Dantzig selector, High-dimensional instrumental variables regression and confidence sets, Sure independence screening in generalized linear models with NP-dimensionality, Nonparametric independence screening in sparse ultra-high dimensional additive models, Principled sure independence screening for Cox models with ultra-high-dimensional covariates, Feature screening via distance correlation learning, A survey of dimension reduction techniques, Efficiency of coordinate descent methods on huge-scale optimization problems, Fast global convergence of gradient methods for high-dimensional statistical recovery, Regularized M-estimators with nonconvexity: statistical and algorithmic theory for local optima, Baltimore, MD: The Johns Hopkins University Press, Extensions of Lipschitz mappings into a Hilbert space, Sparse MRI: the application of compressed sensing for rapid MR imaging, Gradient projection for sparse reconstruction: application to compressed sensing and other inverse problems, CUR matrix decompositions for improved data analysis, On the class of elliptical distributions and their applications to the theory of portfolio choice, In search of non-Gaussian components of a high-dimensional distribution, Scale-Invariant Sparse PCA on High Dimensional Meta-elliptical Data, High-dimensional regression with noisy and missing data: provable guarantees with nonconvexity, Factor modeling for high-dimensional time series: inference for the number of factors, Principal component analysis on non-Gaussian dependent data, Oracle inequalities for the lasso in the Cox model. In fact, most surveys find that the number of organizations experiencing a measurable financial benefit from their big data analytics lags behind the number of organizations implementing big data analytics. Big data analytics in healthcare involves many challenges of different kinds concerning data integrity, security, analysis and presentation of data. To handle these challenges, it is urgent to develop statistical methods that are robust to data complexity (see, for example, [115–117]), noises [62–119] and data dependence [51,120–122]. The core elements of the big data platform is to handle the data in new ways as compared to the traditional relational database. In fact, any finite number of high-dimensional random vectors are almost orthogonal to each other. With so many systems and frameworks, there is a growing and immediate need for application developers who have knowledge in all these systems. \end{equation}, \begin{eqnarray} 6 Data Challenges Managers and Organizations Face ... Senior leaders salivate at the promise of Big Data for developing a competitive edge, ... data-crunching applications, crunching dirty data leads to flawed decisions. It is basically an analysis of the high volume of data which cause computational and data handling challenges. Big data is the base for the next unrest in the field of Information Technology. At the same time it is important to remember that when developers cannot address fundamental data architecture and data management challenges, the ability to take a company to the next level of growth is severely affected. \min _{\beta _{j}}\left \lbrace \ell _{n}(\boldsymbol {\beta }) + \sum _{j=1}^d w_{k,j} |\beta _j|\right \rbrace , ALL RIGHTS RESERVED. As data size may increase depending on time and cycle, ensuring that data is adapted in a proper manner is a critical factor in the success of any company. \mathcal {C}_n = \lbrace \boldsymbol {\beta }\in \mathbb {R}^d: \Vert \mathbf {X}^T (\boldsymbol {\it y}- \mathbf {X}\boldsymbol {\beta }) \Vert _\infty \le \gamma _n\rbrace , However, in big data there are a number of disruptive technology in the world today and choosing from them might be a tough task. 1. Here ‘RP’ stands for the random projection and ‘PCA’ stands for the principal component analysis. 5. {Y = \sum _{j}\beta _{j}X_{j}+ \varepsilon ,} \nonumber\\ Technical challenges: Quality of data: When there is a collection of a large amount of data and storage of this data, it comes at a cost. To illustrate the usefulness of RP, we use the gene expression data in the ‘Incidental endogeneity’ section to compare the performance of PCA and RP in preserving the relative distances between pairwise data points. Oxford University Press is a department of the University of Oxford. {P_{\lambda , \gamma }(\beta _j) \approx P_{\lambda , \gamma }\left(\beta ^{(k)}_{j}\right)}\nonumber\\ This is because data is not in sync it can result in analyses that are wrong and invalid. Iqbal et al. By Irene Makaranka; June 15, 2018; As a data analytics researcher, I know that implementing real-time analytics is a huge task for most enterprises, especially for those dealing with big data. These approaches are generally lumped into a category that is called NoSQL framework that is different from the conventional relational database management system. \end{eqnarray}, The idea of MapReduce is illustrated in Fig.Â, \begin{equation*} Capturing data that is clean, complete, accurate, and formatted correctly for use in multiple systems is an ongoing battle for organizations, many of which aren’t on the winning side of the conflict.In one recent study at an ophthalmology clinic, EHR data ma… The problems with business data analysis are not only related to analytics by itself, but can also be caused by deep system or infrastructure problems. -{\rm QL}(\boldsymbol {\beta })+\lambda \Vert \boldsymbol {\beta }\Vert _0, Of the 85% of companies using Big Data, only 37% have been successful in data-driven insights. Several companies are using additional security measures such as identity and access control, data segmentation, and encryption. Poor classification is due to the existence of many weak features that do not contribute to the reduction of classification error [, \begin{eqnarray} Selection of Appropriate Tools Or Technology For Data Analysis {\rm and} \ \mathbb {E} (\varepsilon X_{j}) = 0 \quad \ {\rm for} \ j=1,\ldots , d, In the last decade, big data has come a very long way and overcoming these challenges is going to be one of the major goals of Big data analytics industry in the coming years. The amount of data produced in every minute makes it challenging to store, manage, utilize, and analyze it. We extract the top 100, 500 and 2500 genes with the highest marginal standard deviations, and then apply PCA and RP to reduce the dimensionality of the raw data to a small number k. Figure 11 shows the median errors in the distance between members across all pairs of data vectors. 12 Challenges of Data Analytics and How to Fix Them 1. Before even going towards implementation, companies must a good amount of time in explaining the benefits and features of business analytics to individuals within the organizations including stakeholders, management and IT teams. We selectively overview several unique features brought by Big Data and discuss some solutions. \lambda _1 p_1\left(y;\boldsymbol {\theta }_1(\mathbf {x})\right)+\cdots +\lambda _m p_m\left(y;\boldsymbol {\theta }_m(\mathbf {x})\right), \ \ chemotherapy) benefit a subpopulation and harm another subpopulation. Big data challenges are numerous: Big data projects have become a normal part of doing business — but that doesn't mean that big data is easy. Quite often, big data adoption projects put security off till later stages. \end{array} \min _{\boldsymbol {\beta }\in \mathcal {C}_n } \Vert \boldsymbol {\beta }\Vert _1 = \min _{ \Vert \ell _n^{\prime }(\boldsymbol {\beta })\Vert _\infty \le \gamma _n } \Vert \boldsymbol {\beta }\Vert _1. The data tools must help companies to not just have access to the required information but also eliminate the need for custom coding. \boldsymbol {\it X}_1, & \ldots & ,\boldsymbol {\it X}_{n} \sim N_d(\boldsymbol {\mu }_1,\mathbf {\it I}_d) \nonumber\\ For example, assuming each covariate has been standardized, we denote, \begin{equation} All this means that while this sector will have multiple job opening, there will be very few experts who will actually have the knowledge to effectively fill these positions. Big data analytics also bear challenges due to the existence of noise in data where the data consists of high degrees of uncertainty and outlier artifacts. To better illustrate this point, we introduce the following mixture model for the population: \begin{eqnarray} \end{eqnarray}, \begin{equation} {\mathbb {E}}(\varepsilon |\lbrace X_j\rbrace _{j\in S}) &= & {\mathbb {E}}\Bigl (Y-\sum _{j\in S}\beta _{j}X_{j} | \lbrace X_j\rbrace _{j\in S}\Bigr )\nonumber\\ As big data is still in its evolution stage, there are many companies that are developing new techniques and methods in the field of big data analytics. In this digitalized world, we are producing a huge amount of data in every minute. The key to data value creation is Big Data Analytics and that is why it is important to focus on that aspect of analytics. Data is a very valuable asset in the world today. Dependent data challenge: in various types of modern data, such as financial time series, fMRI and time course microarray data, the samples are dependent with relatively weak signals. This is a new set of complex technologies, while still in the nascent stages of development and evolution. In a regression setting, \begin{eqnarray} As big data technology is … \end{equation}, Suppose that the data information is summarized by the function ℓ, \begin{equation} The challenge of the need for synchronization across data sources: Once data is integrated into a big platform, data copies migrated from different sources at different rates and schedules can sometimes be out of sync within the entire system. If inconsistent data is produced at any stage it can result in inconsistencies at all stages and have completely disastrous results. The authors of [111] further simplified the RP procedure by removing the unit column length constraint. This lack of knowledge will result in less than successful implementations of data and analytical processes within a company/brand. \ell _n(\boldsymbol {\beta })+\sum _{j=1}^d P_{\lambda ,\gamma }(\beta _j), Wrong insights can damage a company to a great degree, sometimes even more than not having the required data insights. +\, P_{\lambda , \gamma }^{\prime }\left(\beta ^{(k)}_{j}\right) \left(|\beta _j| - |\beta ^{(k)}_{j}|\right). From preventing fraud to gaining a competitive edge over competitors to helping retain more customers and anticipating business demands- the possibilities with business analytics are endless. As data grows inside, it is important that companies understand this need and process it in an effective manner. One thing to note is that RP is not the ‘optimal’ procedure for traditional small-scale problems. \end{equation}, To handle the computational challenge raised by massive and high-dimensional datasets, we need to develop methods that preserve the data structure as much as possible and is computational efficient for handling high dimensionality. Principal component analysis (PCA) is the most well-known dimension reduction method. We also refer to [101] and [102] for research studies in this direction. Key Big Data Challenges for The Healthcare Sector. According to an IDC study, the success of big data and analytics can be driven by increased collaboration, particularly among IT, line-of-business, and analytics groups. \mathbf {y}=\mathbf {X}\boldsymbol {\beta }+\boldsymbol {\epsilon },\quad \mathrm{Var}(\boldsymbol {\epsilon })=\sigma ^2\mathbf {I}_d, Organizations today independent of their size are making gigantic interests in the field of big data analytics. As companies have a lot of data, understanding that data is very important because without that basic knowledge it is difficult to integrate it with the business data analytics programme. However, enforcing R to be orthogonal requires the Gram–Schmidt algorithm, which is computationally expensive. Not all organizations can afford these costs. Data integration: the ultimate challenge? This justifies the RP when R is indeed a projection matrix. 4. As companies look to adequately protect themselves against the growing threat of cybercrime and handle ever-growing volumes of data, the value of the market will … All data comes from somewhere, but unfortunately for many healthcare providers, it doesn’t always come from somewhere with impeccable data governance habits. \widehat{\sigma }^2 = \frac{\boldsymbol {\it y}^T (\mathbf {I}_n - \mathbf {P}_{\widehat{ S}}) \boldsymbol {\it y}}{ n - |\widehat{S }|}. The International Neuroimaging Data-sharing Initiative (INDI) and the Functional Connectomes Project, The autism brain imaging data exchange: Towards a large-scale evaluation of the intrinsic brain architecture in autism, The ADHD-200 Consortium. Volume — The larger the volume of data, the higher the risk and difficulty associated with it in terms of its management. Here are of the topmost challenges faced by healthcare providers using big data. rare diseases or diseases in small populations) and understanding why certain treatments (e.g. These include. Data Analytics is a qualitative and quantitative technique which is used to embellish the productivity of the business. As "data" is the key word in big data, one must understand the challenges involved with the data itself in detail. \end{eqnarray}, \begin{eqnarray} \end{eqnarray}, To explain the endogeneity problem in more detail, suppose that unknown to us, the response, \begin{equation*} For Permissions, please email: This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, Regulating off-centering distortion maximizes photoluminescence in halide perovskites, More is different: how aggregation turns on the light, A high-capacity cathode for rechargeable K-metal battery based on reversible superoxide-peroxide conversion, Plasmonic evolution of atomically size-selected Au clusters by electron energy loss spectrum, Using bioorthogonally catalyzed lethality strategy to generate mitochondria-targeting antitumor metallodrugs, |$\boldsymbol {\it Z}\in {\mathbb {R}}^d$|, |$\mathbf {X}=[\mathbf {x}_1,\ldots ,\mathbf {x}_n]^{\rm T}\in {\mathbb {R}}^{n\times d}$|, |$\boldsymbol {\epsilon }\in {\mathbb {R}}^n$|, |$\boldsymbol {\it X}=(X_1,\ldots ,X_d)^T \sim N_d({\boldsymbol 0},\mathbf {I}_d)$|â, |$\widehat{\mathrm{Corr}}\left(X_{1}, X_{j} \right)$|, |$Y=\sum _{j=1}^{d}\beta _j X_{j}+\varepsilon$|â, |$\widehat{\mathrm{Corr}}(X_j, \widehat{\varepsilon })$|â, |$\sum _{j=1}^d P_{\lambda ,\gamma }(\beta _j)$|, |$\ell (\boldsymbol {\beta }) = \mathbb {E}\ell _n(\boldsymbol {\beta })$|â, |$\ell _n (\boldsymbol {\beta }) = \Vert \boldsymbol {y}- \mathbf {X}\boldsymbol {\beta }\Vert ^2_{2}$|â, |$\ell _n^{\prime }(\boldsymbol {\beta }) = 0$|, |$\widehat{\mathrm{Corr}}(X_j, \widehat{\varepsilon })$|, |$\widehat{\mathrm{Corr}}(X_j^2, \widehat{\varepsilon })$|, |$\widehat{\boldsymbol {\beta }}^{(k)} = (\beta ^{(k)}_{1}, \ldots , \beta ^{(k)}_{d})^{\rm T}$|, |$w_{k,j} = P_{\lambda , \gamma }^{\prime }(\beta ^{(k)}_{j})$|â, |$\widehat{\mathbf {U}}_k\in {\mathbb {R}}^{d\times k}$|â, |$\mathbf {R}\in {\mathbb {R}}^{d\times k}$|, GOALS AND CHALLENGES OF ANALYZING BIG DATA, http://creativecommons.org/licenses/by/4.0/, Receive exclusive offers and updates from Oxford Academic, Copyright © 2020 China Science Publishing & Media Ltd. (Science Press). With the rising popularity of Big data analytics, it is but obvious that investing in this medium is what is going to secure the future growth of companies and brands. On one hand, Big Data hold great promises for discovering subtle population patterns and heterogeneities that are not possible with small-scale data. Challenges of Big Data Analytics. The Challenges in Using Big Data Analytics: The biggest challenge in using big data analytics is to segment useful data from clusters. There are number of different NoSQL approaches available in the company from using methods like hierarchal object representation to graph databases that can maintain interconnected relationships between different objects. here we will discuss the Challenges of Big Data Analytics. 6 Challenges to Implementing Big Data and Analytics Big data is usually defined in terms of the “3Vs”: data that has large volume, velocity, and variety. The idea on studying statistical properties based on computational algorithms, which combine both computational and statistical analysis, represents an interesting future direction for Big Data. We then project the n × d data matrix D to this linear subspace to obtain an n × k data matrix |$\mathbf {D}\widehat{\mathbf {U}}_k$|⁠. While Big Data offers a ton of benefits, it comes with its own set of issues. Computationally, the approximate regularization path following algorithm attains a global geometric rate of convergence for calculating the full regularization path, which is fastest possible among all first-order algorithms in terms of iteration complexity. Search for other works by this author on: Big Data are often created via aggregating many data sources corresponding to different subpopulations. This procedure is optimal among all the linear projection methods in minimizing the squared error introduced by the projection. On one hand, Big Data hold great promises for discovering subtle population patterns and heterogeneities that are not possible with small-scale data. Lack of Understanding of Big Data, Quality of Data, Integration of Platform are the challenges in big data analytics. One of the most important challenges in Big Data Implementation continues to be security. 3. \widehat{r} =\max _{j\ge 2} |\widehat{\mathrm{Corr}}\left(X_{1}, X_{j} \right)\!|, \end{eqnarray}, Furthermore, we can compute the maximum absolute multiple correlation between, \begin{eqnarray} Challenge #5: Dangerous big data security holes. Assuming that all the aforementioned hurdles can be overcome, and with data in-hand to complete our big-data analysis of breast cancer outcomes in the context of prognostic genes and their mutations, how do we integrate big data with clinical data to truly obtain new knowledge or information that can be further tested in the appropriate follow-on study? The data required for analysis is a combination of both organized and unorganized data which is very hard to comprehend. However, the use and analysis of big data must be based on accurate and high-quality data, which is a necessary condition for generating value from big data. We introduce several dimension (data) reduction procedures in this section. To balance the statistical accuracy and computational complexity, the suboptimal procedures in small- or medium-scale problems can be ‘optimal’ in large scale. With today’s data-driven organizations and the introduction of big data, risk managers and other employees are often overwhelmed with the amount of data that is collected. While these challenges might seem big, it is important to address them in an effective manner because everyone knows that business analytics can truly change the fortune of a company. \begin{array}{lll} That’s why risk managers should look toward flexible tools that offer a 360º view of data and leverage integrated processing and analysis capabilities. So many examples little space. The authors of [104] showed that if points in a vector space are projected onto a randomly selected subspace of suitable dimensions, then the distances between the points are approximately preserved. Learn hadoop skills like HBase, Hive, Pig, Mahout. Would the field of cognitive neuroscience be advanced by sharing functional MRI data? This paper discusses statistical and computational aspects of Big Data analysis. This article will look at these challenges in a closer manner and understand how companies can tackle these challenges in an effective fashion. With amazing potential, big data is today an emerging disruptive force that is poised to become the next big thing in the field of integrated analytics, thereby transforming the manner in which brands and companies perform their duties across stages and economies. This paper gives overviews on the salient features of Big Data and how these features impact on paradigm change on statistical and computational methods as well as computing architectures. The authors gratefully acknowledge Dr Emre Barut for his kind assistance on producing Fig. \end{equation}, Incidental endogeneity is another subtle issue raised by high dimensionality. According to Gartner, 87% of companies have low BI (business intelligence) and analytics maturity, lacking data guidance and support. Here we have discussed the Different challenges of Big Data analytics. Each subpopulation might exhibit some unique features not shared by others. Implementing a big data analytics solution isn't always as straightforward as companies hope it will be. This has been a guide to the Challenges of Big Data analytics. \end{equation*}, The case for cloud computing in genome informatics, High-dimensional data analysis: the curses and blessings of dimensionality, Discussion on the paper ‘Sure independence screening for ultrahigh dimensional feature space’ by Fan and Lv, High dimensional classification using features annealed independence rules, Theoretical measures of relative performance of classifiers for high dimensional data with small sample sizes, Regression shrinkage and selection via the lasso, Variable selection via nonconcave penalized likelihood and its oracle properties, The Dantzig selector: statistical estimation when, Nearly unbiased variable selection under minimax concave penalty, Sure independence screening for ultrahigh dimensional feature space (with discussion), Using generalized correlation to effect variable selection in very high dimensional problems, A comparison of the lasso and marginal regression, Variance estimation using refitted cross-validation in ultrahigh dimensional regression, Posterior consistency of nonparametric conditional moment restricted models, Features of big data and sparsest solution in high confidence set, Optimally sparse representation in general (nonorthogonal) dictionaries via, Gradient directed regularization for linear regression and classification, Penalized regressions: the bridge versus the lasso, Coordinate descent algorithms for lasso penalized regression, An iterative thresholding algorithm for linear inverse problems with a sparsity constraint, A fast iterative shrinkage-thresholding algorithm for linear inverse problems, Optimization transfer using surrogate objective functions, One-step sparse estimates in nonconcave penalized likelihood models, Ultrahigh dimensional feature selection: beyond the linear model, Distributed optimization and statistical learning via the alternating direction method of multipliers, Distributed graphlab: a framework for machine learning and data mining in the cloud, Making a definitive diagnosis: successful clinical application of whole exome sequencing in a child with intractable inflammatory bowel disease, Personal omics profiling reveals dynamic molecular and medical phenotypes, Multiple rare alleles contribute to low plasma levels of HDL cholesterol, A data-adaptive sum test for disease association with multiple common or rare variants, An overview of recent developments in genomics and associated statistical methods, Capturing heterogeneity in gene expression studies by surrogate variable analysis, Controlling the false discovery rate: a practical and powerful approach to multiple testing, The positive false discovery rate: a Bayesian interpretation and the q-value, Empirical null and false discovery rate analysis in neuroimaging, Correlated z-values and the accuracy of large-scale statistical estimates, Control of the false discovery rate under arbitrary covariance dependence, Gene expression omnibus: NCBI gene expression and hybridization array data repository, What has functional neuroimaging told us about the mind?

challenges of big data analysis

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