Titlebook: Big and Complex Data Analysis; Methodologies and Ap S. Ejaz Ahmed Book 2017 Springer International Publishing AG 2017 big data analysis.com

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A Computationally Efficient Approach for Modeling Complex and Big Survival Datany existing methods fail because of lack of computational power. The finite-sample properties and the utility of the proposed method are examined through an extensive simulation study and an analysis of the national kidney transplant data.

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Tests of Concentration for Low-Dimensional and High-Dimensional Directional Datations only, as well as “pseudo-FvML” versions of such tests, that meet asymptotically the nominal level constraint within the whole class of rotationally symmetric distributions. We conduct a Monte-Carlo study to check our asymptotic results and to investigate the finite-sample behavior of the proposed tests.

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Analysis of Correlated Data with Error-Prone Response Under Generalized Linear Mixed Modelsignored, and evaluate the performance of an intuitively appealing approach for correction of response error effects. We develop likelihood methods to correct for effects induced from response error. Simulation studies are conducted to evaluate the performance of the proposed methods, and a real data set is analyzed with the proposed methods.

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Statistical Process Control Charts as a Tool for Analyzing Big Dataoduce some basic SPC charts and some of their modifications, and describe how these charts can be used for monitoring different types of processes. Among many potential applications, dynamic disease screening and profile/image monitoring will be discussed in some detail.

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Nonparametric Testing for Heterogeneous Correlationhe number of parameters used in the test grows with .. An analysis of wine quality illustrates how the methods detect heterogeneity of association between chemical properties of the wine, which are attributable to a mix of different cultivars.

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Optimal Shrinkage Estimation in Heteroscedastic Hierarchical Linear Modelsasymptotically) optimal under mean squared error loss in each model. Simulation study is conducted to compare the performance of the proposed methods with existing shrinkage estimators. We also apply the method to real data and obtain encouraging and interesting results.

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Unsupervised Bump Hunting Using Principal ComponentsRIM) under normality which facilitates the theoretical studies to follow. Using basic geometrical arguments, we then demonstrate how the Principal Components rotation of the predictor space alone can in fact generate improved mode estimators. Simulation results are used to illustrate our findings.

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Introduction to Nursing Informaticsf two selected submodels. Such a pretest and shrinkage strategy is constructed by shrinking an overfitted model estimator in the direction of an underfitted model estimator. The numerical studies indicate that our post-selection pretest and shrinkage strategy improved the prediction performance of selected submodels.