[PDF] Top 20 Contributions to Statistical Methods for High Dimensional and Dependent Data.
Has 10000 "Contributions to Statistical Methods for High Dimensional and Dependent Data." found on our website. Below are the top 20 most common "Contributions to Statistical Methods for High Dimensional and Dependent Data.".
Contributions to Statistical Methods for High Dimensional and Dependent Data.
... single data realization are similar to those averages reported in Table ...particular data, the quality of the estimated signal under the correct working correlation structure is superior than the ones ... See full document
139
High Dimensional Methods in Statistics, Data Mining and Finance.
... in statistical learning problems ...multi dimensional data visualization and develop a multidimensional boxplot in Chapter ...allocating data points to a number of subclasses so that the ... See full document
141
Statistical Methods for High-Dimensional, Spatially-Distributed Microbiome Data from Next-Generation Sequencing.
... introduce statistical models that are capable of predicting the origin of a sample with high accuracy based solely on the sample’s unique collection of fungal ... See full document
103
Statistical Methods for Functional and Complex Data.
... popular methods for smoothing the sample covariance ...for high-dimensional dense functional data and the extension to sparse data is far from ... See full document
162
Advances in Bayesian Methods for High-Dimensional Environmental Data.
... The data for each chemical-assay combination consists of dose-response pairs and are often known to have a monotonic ...the data for each chemical-assay combination can be sparse. Hence, HTS data ... See full document
123
Hofner, Benjamin (2011): Boosting in structured additive models. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik
... in statistical modeling during the last two decades make it possible to determine relationships in complex, high-dimensional data sets that are hard to handle by using classical methods ... See full document
168
High Dimensional Generalized Empirical Likelihood for Moment Restrictions with Dependent Data
... related methods under high dimensionality of both the moment restrictions and the parameters of ...under high dimensional ...under high dimensionality and sparsity ...independent ... See full document
45
Feature optimization in high dimensional chemical space: statistical and data mining solutions
... input data thereby, improving upon the statistical parameter values as ...other methods like Eli Lilly filter and SOM, two molecules were screened out for further pro- cesses in the drug discovery ... See full document
7
Feature Selection and Classification of Microarray Gene Expression Data of Ovarian Carcinoma Patients using Weighted Voting Support Vector Machine
... reduce high-dimensional data by statistical method to select valuable genes with high impact as biomarkers and then classify ovarian tumor based on gene expression data of two ... See full document
13
Statistical Methods for High Dimensional Count and Compositional Data With Applications to Microbiome Studies
... In microbiome studies, taxa composition is often estimated based on the sequencing read counts in order to account for the large variability in the total number of observed reads across different samples. Due to ... See full document
113
Contributions to Statistical Methods for Functional Data Analysis and Generalized Additive Model.
... of data collection and storage, the data in the form of a curve, a surface, or anything that varies over a continuum become more and more readily available to ...of data as “functional data” ... See full document
81
Survey: Effective Feature Subset Selection Methods and Algorithms for High Dimensional Data
... Relief is well known and good feature set estimator. Feature set estimators evaluate features individually. The fundamental idea of Relief algorithm [4], [5] is estimate the quality of subset of features by comparing the ... See full document
7
Analysis of Penalized Regression Methods in a Simple Linear Model on the High-Dimensional Data
... technology, data are turning into high-dimensional data that may cause many problems in various research, scientific, medical and engineering fields ...conventional data, this type of ... See full document
8
Statistical Methods For Truncated Survival Data
... many methods exist to adjust for either left or right truncation, there are very few methods that adjust for double ...time-to-event data is doubly truncated, the regression coefficient estimators ... See full document
124
Contributions to High Performance Big Data Computing
... Major challenges. Digital pathology images are produced at an extremely high resolution. A typical 2D pathology image may contain 100,000 x 100,000 pixels, with a million micro-anatomic objects. A typical 3D ... See full document
53
Statistical Methods of Neuroimaging Data Analysis.
... then performed, followed by cerebellum removal. We visually checked the skull-stripped images to ensure clean and dura removal. We next employed FAST of the FSL package ( http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/ ) to ... See full document
107
Statistical Inference For High-Dimensional Linear Models
... the statistical inference problem in the high-dimensional instrumental variable framework with possibly invalid ...IV methods, especially when the instruments are ... See full document
253
Statistical Analysis Methods for the fMRI Data
... Finding the answer to the question "whether two or more regions of the brain are associated with each other" is important. Activation pattern of the blood-oxygen-level-dependent (BOLD) response to task and ... See full document
8
Feature Subset Selection Methods for High Dimensional Data B Anitha & B Venkataramana
... the data are selected for application of a learning ...suitably high accuracy in representing the original features, learning from data techniques can ...and data mining ... See full document
5
High-dimensional Statistical Inference: from Vector to Matrix
... Dantzig selector for sparse signal recovery, were proposed and analyzed (Keshavan et al., 2010; Mazumder et al., 2010; Koltchinskii, 2011; Koltchinskii et al., 2011; Rohde and Tsybakov, 2011). In many applications, the ... See full document
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