Variable Screening Methods In Multi Category Problems For Ultra High Dimensional Data

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Variable Screening Methods in Multi-Category Problems for Ultra-High Dimensional Data

Author : Yue Zeng
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (1 download)

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Book Synopsis Variable Screening Methods in Multi-Category Problems for Ultra-High Dimensional Data by : Yue Zeng

Download or read book Variable Screening Methods in Multi-Category Problems for Ultra-High Dimensional Data written by Yue Zeng and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Variable screening techniques are fast and crude techniques to scan high-dimensional data and conduct dimension reduction before a refined variable selection method is applied. Its marginal analysis feature makes the method computationally feasible for ultra-high dimensional problems. However, most existing screening methods for classification problems are designed only for binary classification problems. There is lack of a comprehensive study on variable screening for multi-class classification problems. This research aims to fill the gap by developing variable screening for multi-class problems, to meet the need of high dimensional classification. The work has useful applications in cancer study, medicine, engineering and biology. In this research, we propose and investigate new and effective screening methods for multi-class classification problems. We consider two types of screening methods. The first one conducts screening for multiple binary classification problems separately and then aggregates the selected variables. The second one conducts screening for multi-class classification problems directly. In particular, for each method we investigate important issues such as choices of classification algorithms, variable ranking, and model size determination. We implement various selection criteria and compare their performance. We conduct extensive simulation studies to evaluate and compare the proposed screening methods with existing ones, which show that the new methods are promising. Furthermore, we apply the proposed methods to four cancer studies. R code has been developed for each method.

Feature Screening and Variable Selection for Ultrahigh Dimensional Data Analysis

Author : Wei Zhong
Publisher :
ISBN 13 :
Total Pages : 155 pages
Book Rating : 4.:/5 (82 download)

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Book Synopsis Feature Screening and Variable Selection for Ultrahigh Dimensional Data Analysis by : Wei Zhong

Download or read book Feature Screening and Variable Selection for Ultrahigh Dimensional Data Analysis written by Wei Zhong and published by . This book was released on 2012 with total page 155 pages. Available in PDF, EPUB and Kindle. Book excerpt:

The Collected Works of Wassily Hoeffding

Author : Wassily Hoeffding
Publisher : Springer Science & Business Media
ISBN 13 : 1461208653
Total Pages : 653 pages
Book Rating : 4.4/5 (612 download)

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Book Synopsis The Collected Works of Wassily Hoeffding by : Wassily Hoeffding

Download or read book The Collected Works of Wassily Hoeffding written by Wassily Hoeffding and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 653 pages. Available in PDF, EPUB and Kindle. Book excerpt: It has been a rare privilege to assemble this volume of Wassily Hoeffding's Collected Works. Wassily was, variously, a teacher, supervisor and colleague to us, and his work has had a profound influence on our own. Yet this would not be sufficient reason to publish his collected works. The additional and overwhelmingly compelling justification comes from the fun damental nature of his contributions to Statistics and Probability. Not only were his ideas original, and far-reaching in their implications; Wassily de veloped them so completely and elegantly in his papers that they are still cited as prime references up to half a century later. However, three of his earliest papers are cited rarely, if ever. These include material from his doctoral dissertation. They were written in German, and two of them were published in relatively obscure series. Rather than reprint the original articles, we have chosen to have them translated into English. These trans lations appear in this book, making Wassily's earliest research available to a wide audience for the first time. All other articles (including those of his contributions to Mathematical Reviews which go beyond a simple reporting of contents of articles) have been reproduced as they appeared, together with annotations and corrections made by Wassily on some private copies of his papers. Preceding these articles are three review papers which dis cuss the . impact of his work in some of the areas where he made major contributions.

Feature Screening For Ultra-high Dimensional Longitudinal Data

Author : Wanghuan Chu
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (959 download)

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Book Synopsis Feature Screening For Ultra-high Dimensional Longitudinal Data by : Wanghuan Chu

Download or read book Feature Screening For Ultra-high Dimensional Longitudinal Data written by Wanghuan Chu and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: High and ultrahigh dimensional data analysis is now receiving more and more attention in many scientific fields. Various variable selection methods have been proposed for high dimensional data where feature dimension p increases with sample size n at polynomial rates. In ultrahigh dimensional setting, p is allowed to grow with n at an exponential rate. Instead of jointly selecting active covariates, a more effective approach is to incorporate screening rule that aims at filtering out unimportant covariates through marginal regression techniques. This thesis is concerned with feature screening methods for ultrahigh dimensional longitudinal data. Such data occur frequently in longitudinal genetic studies, where phenotypes and some covariates are measured repeatedly over a certain time period. Along with the genetic measurements, longitudinal genetic studies provide valuable resources for exploring primary genetic and environmental factors that influence complex phenotypes over time. The proposed statistical methods in this work allow us not only to identify genetic determinants of common complex disease, but also to understand at which stage of human life do the genetic determinants become important. In Chapter 3, we propose a new feature screening procedure for ultrahigh dimensional time-varying coefficient models. We present an effective screening rule based on marginal B-spline regression that incorporates time-varying variance and within-subject correlations. We show that under certain conditions, this procedure possesses sure screening property, and the false selection rates can be controlled. We demonstrate how within subject variability can be harnessed for increasing screening accuracy by Monte Carlo simulation studies. Furthermore, we illustrate the proposed screening rule via an empirical analysis of the Childhood Asthma Management Program (CAMP) data. Our empirical analysis clearly shows that the proposed approach is especially useful for such studies as children change quite extensively over a four-year period with highly nonlinear patterns. In Chapter 4, we study screening rules for ultrahigh dimensional covariates that are potentially associated with random effects. Mixed effects models are popular for taking into account the dependence structure of longitudinal data, as subject-specific random effects can explicitly account for within-subject correlation. We propose a two-step screening procedure for generalized varying-coefficient mixed effects models. The two-step procedure screens fixed effects first and then random effects. We conduct simulation studies to assess the finite sample performance of this two-step screening approach for continuous response with linear regression, binary response with logistic regression, count response with Poisson regression, and ordinal response with proportional-odds cumulative logit model. In real data application, we apply this procedure to data from Framingham Heart Study (FHS), and explore the genetic and environmental effects on body mass index (BMI), obesity and blood pressure in three separate analyses. Our results confirm some findings from previous studies, and also identify genetic markers with highly significant effects and interesting time-dependent patterns that worth further exploration.

Statistical Inference

Author : Ayanendranath Basu
Publisher : CRC Press
ISBN 13 : 1420099663
Total Pages : 424 pages
Book Rating : 4.4/5 (2 download)

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Book Synopsis Statistical Inference by : Ayanendranath Basu

Download or read book Statistical Inference written by Ayanendranath Basu and published by CRC Press. This book was released on 2011-06-22 with total page 424 pages. Available in PDF, EPUB and Kindle. Book excerpt: In many ways, estimation by an appropriate minimum distance method is one of the most natural ideas in statistics. However, there are many different ways of constructing an appropriate distance between the data and the model: the scope of study referred to by "Minimum Distance Estimation" is literally huge. Filling a statistical resource gap, Stati

Trends and Challenges in Categorical Data Analysis

Author : Maria Kateri
Publisher : Springer Nature
ISBN 13 : 3031311868
Total Pages : 323 pages
Book Rating : 4.0/5 (313 download)

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Book Synopsis Trends and Challenges in Categorical Data Analysis by : Maria Kateri

Download or read book Trends and Challenges in Categorical Data Analysis written by Maria Kateri and published by Springer Nature. This book was released on 2023-07-08 with total page 323 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a selection of modern and sophisticated methodologies for the analysis of large and complex univariate and multivariate categorical data. It gives an overview of a substantive and broad collection of topics in the analysis of categorical data, including association, marginal and graphical models, time series and fixed effects models, as well as modern methods of estimation such as regularization, Bayesian estimation and bias reduction methods, along with new simple measures for model interpretability. Methodological innovations and developments are illustrated and explained through real-world applications, together with useful R packages, allowing readers to replicate most of the analyses using the provided code. The applications span a variety of disciplines, including education, psychology, health, economics, and social sciences.

Application of Novel Statistical and Machine-learning Methods to High-dimensional Clinical Cancer and (Multi-)Omics data

Author : Chao Xu
Publisher : Frontiers Media SA
ISBN 13 : 2889714365
Total Pages : 136 pages
Book Rating : 4.8/5 (897 download)

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Book Synopsis Application of Novel Statistical and Machine-learning Methods to High-dimensional Clinical Cancer and (Multi-)Omics data by : Chao Xu

Download or read book Application of Novel Statistical and Machine-learning Methods to High-dimensional Clinical Cancer and (Multi-)Omics data written by Chao Xu and published by Frontiers Media SA. This book was released on 2022-02-02 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Developing a Protocol for Observational Comparative Effectiveness Research: A User's Guide

Author : Agency for Health Care Research and Quality (U.S.)
Publisher : Government Printing Office
ISBN 13 : 1587634236
Total Pages : 236 pages
Book Rating : 4.5/5 (876 download)

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Book Synopsis Developing a Protocol for Observational Comparative Effectiveness Research: A User's Guide by : Agency for Health Care Research and Quality (U.S.)

Download or read book Developing a Protocol for Observational Comparative Effectiveness Research: A User's Guide written by Agency for Health Care Research and Quality (U.S.) and published by Government Printing Office. This book was released on 2013-02-21 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: This User’s Guide is a resource for investigators and stakeholders who develop and review observational comparative effectiveness research protocols. It explains how to (1) identify key considerations and best practices for research design; (2) build a protocol based on these standards and best practices; and (3) judge the adequacy and completeness of a protocol. Eleven chapters cover all aspects of research design, including: developing study objectives, defining and refining study questions, addressing the heterogeneity of treatment effect, characterizing exposure, selecting a comparator, defining and measuring outcomes, and identifying optimal data sources. Checklists of guidance and key considerations for protocols are provided at the end of each chapter. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews. More more information, please consult the Agency website: www.effectivehealthcare.ahrq.gov)

Online and Offline Feature Screening and Applications

Author : Mingyuan Wang
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (134 download)

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Book Synopsis Online and Offline Feature Screening and Applications by : Mingyuan Wang

Download or read book Online and Offline Feature Screening and Applications written by Mingyuan Wang and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Filter or screening methods are often used as a preprocessing step for reducing the number of variables used by a learning algorithm in obtaining a classification or regression model.While there are many such filter methods, there is a need for an objective evaluation of these methods. Such an evaluation is needed to compare them with each other and also to answer whether they are at all useful, or a learning algorithm could do a better job without them. For this purpose, many popular screening methods are partnered in this thesis with three regression learners and five classification learners and evaluated on ten real datasets to obtain accuracy criteria such as R-square and area under the ROC curve (AUC). The obtained results are compared through curve plots and comparison tables in order to find out whether screening methods help improve the performance of learning algorithms and how they fare with each other. Our findings revealed that the screening methods were useful in improving the prediction of the best learner on two regression and two classification datasets out of the ten datasets evaluated. In present days, datasets not only have higher dimension and larger sample size, but also have some unique characteristics that need to be taken into consideration. Signal data of different types, website information data, and others only exist for a short period of time and methods that only focus on dealing with high dimension and large sample size are not adequate to handle this type of data. Therefore a considerable amount online feature selection methods were introduced to handle these kind of problems in recent years. Online screening methods are one of the categories of online feature selection methods. They are used to preprocess data that is too large for batch screening methods to handle or to handle data that comes in sequential order and disappears soon after being processed. Furthermore due to the useful properties of the criteria of some screening methods such as mutual information and Gini index, some online screening methods are often integrated into online learning algorithms. Most online screening methods are concentrated on classification problem. Our research study focuses on classification as well. Researches are conducted to investigate whether online screening methods can obtain identical results as their offline version or not. Several online screening methods were compared with their batch counterparts. Experiments were conducted on classification datasets with binary labels. Results are summarized in different comparison tables to confirm whether online methods can give the exact or very close feature scores as offline methods.

Nonparametric Independence Screening And Test-based Screening Via The Variance Of The Regression Function

Author : Won Song
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (927 download)

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Book Synopsis Nonparametric Independence Screening And Test-based Screening Via The Variance Of The Regression Function by : Won Song

Download or read book Nonparametric Independence Screening And Test-based Screening Via The Variance Of The Regression Function written by Won Song and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation develops procedures for screening variables, in ultrahigh-dimensional settings, based on their predictive significance. First, we review existing literature on the sure screening procedures for analyzing ultrahigh-dimensional data. Second, we develop a screening procedure by ranking the variables, according to the variance of their respective marginal regression functions (RV-SIS). This is in sharp contrast with existing literature on feature screening, which ranks the variables according to some correlation measures with the response, and hence select variables with no predictive power (e.g., variables that influence aspects of the conditional distribution of the response other than the regression function). The RV-SIS is easy to implement and does not require any model specification for the regression functions (such as linear or other semi-parametric modeling). We show that, under some mild technical conditions, the RV-SIS possesses a sure independence property, which is defined by Fan and Lv (2008). Numerical comparisons suggest that RV-SIS has competitive performance compared to other screening procedure and outperforms them in many different model settings. Third, we develop a test procedure for the hypothesis of a constant regression function, and also a test-based variable screening procedure. We study the asymptotic theory for the variance of the regression function and use it to introduce a new test procedure for testing the significance of a predictor. Using the set of p-values, we introduce a variable screening procedure with a specified desirable false discovery rate by using Benjamini and Hochberg (1995) approach.

Feature Screening for Ultrahigh Dimensional Categorical Data with Applications

Author : Danyang Huang
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (137 download)

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Book Synopsis Feature Screening for Ultrahigh Dimensional Categorical Data with Applications by : Danyang Huang

Download or read book Feature Screening for Ultrahigh Dimensional Categorical Data with Applications written by Danyang Huang and published by . This book was released on 2014 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ultrahigh dimensional data with both categorical responses and categorical covariates are frequently encountered in the analysis of big data, for which feature screening has become an indispensable statistical tool. We propose a Pearson chi-square based feature screening procedure for categorical response with ultrahigh dimensional categorical covariates. The proposed procedure can be directly applied for detection of important interaction effects. We further show that the proposed procedure possesses screening consistency property in the terminology of Fan and Lv (2008). We investigate the finite sample performance of the proposed procedure by Monte Carlo simulation studies, and illustrate the proposed method by two empirical datasets.

Mixed Effects Models for Complex Data

Author : Lang Wu
Publisher : CRC Press
ISBN 13 : 9781420074086
Total Pages : 431 pages
Book Rating : 4.0/5 (74 download)

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Book Synopsis Mixed Effects Models for Complex Data by : Lang Wu

Download or read book Mixed Effects Models for Complex Data written by Lang Wu and published by CRC Press. This book was released on 2009-11-11 with total page 431 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although standard mixed effects models are useful in a range of studies, other approaches must often be used in correlation with them when studying complex or incomplete data. Mixed Effects Models for Complex Data discusses commonly used mixed effects models and presents appropriate approaches to address dropouts, missing data, measurement errors, censoring, and outliers. For each class of mixed effects model, the author reviews the corresponding class of regression model for cross-sectional data. An overview of general models and methods, along with motivating examples After presenting real data examples and outlining general approaches to the analysis of longitudinal/clustered data and incomplete data, the book introduces linear mixed effects (LME) models, generalized linear mixed models (GLMMs), nonlinear mixed effects (NLME) models, and semiparametric and nonparametric mixed effects models. It also includes general approaches for the analysis of complex data with missing values, measurement errors, censoring, and outliers. Self-contained coverage of specific topics Subsequent chapters delve more deeply into missing data problems, covariate measurement errors, and censored responses in mixed effects models. Focusing on incomplete data, the book also covers survival and frailty models, joint models of survival and longitudinal data, robust methods for mixed effects models, marginal generalized estimating equation (GEE) models for longitudinal or clustered data, and Bayesian methods for mixed effects models. Background material In the appendix, the author provides background information, such as likelihood theory, the Gibbs sampler, rejection and importance sampling methods, numerical integration methods, optimization methods, bootstrap, and matrix algebra. Failure to properly address missing data, measurement errors, and other issues in statistical analyses can lead to severely biased or misleading results. This book explores the biases that arise when naïve methods are used and shows which approaches should be used to achieve accurate results in longitudinal data analysis.

Advances in Computational Intelligence

Author : Ignacio Rojas
Publisher : Springer
ISBN 13 : 3642386792
Total Pages : 687 pages
Book Rating : 4.6/5 (423 download)

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Book Synopsis Advances in Computational Intelligence by : Ignacio Rojas

Download or read book Advances in Computational Intelligence written by Ignacio Rojas and published by Springer. This book was released on 2013-06-21 with total page 687 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set LNCS 7902 and 7903 constitutes the refereed proceedings of the 12th International Work-Conference on Artificial Neural Networks, IWANN 2013, held in Puerto de la Cruz, Tenerife, Spain, in June 2013. The 116 revised papers were carefully reviewed and selected from numerous submissions for presentation in two volumes. The papers explore sections on mathematical and theoretical methods in computational intelligence, neurocomputational formulations, learning and adaptation emulation of cognitive functions, bio-inspired systems and neuro-engineering, advanced topics in computational intelligence and applications

Categorical Data Analysis

Author : Alan Agresti
Publisher : John Wiley & Sons
ISBN 13 : 1118710940
Total Pages : 756 pages
Book Rating : 4.1/5 (187 download)

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Book Synopsis Categorical Data Analysis by : Alan Agresti

Download or read book Categorical Data Analysis written by Alan Agresti and published by John Wiley & Sons. This book was released on 2013-04-08 with total page 756 pages. Available in PDF, EPUB and Kindle. Book excerpt: Praise for the Second Edition "A must-have book for anyone expecting to do research and/or applications in categorical data analysis." —Statistics in Medicine "It is a total delight reading this book." —Pharmaceutical Research "If you do any analysis of categorical data, this is an essential desktop reference." —Technometrics The use of statistical methods for analyzing categorical data has increased dramatically, particularly in the biomedical, social sciences, and financial industries. Responding to new developments, this book offers a comprehensive treatment of the most important methods for categorical data analysis. Categorical Data Analysis, Third Edition summarizes the latest methods for univariate and correlated multivariate categorical responses. Readers will find a unified generalized linear models approach that connects logistic regression and Poisson and negative binomial loglinear models for discrete data with normal regression for continuous data. This edition also features: An emphasis on logistic and probit regression methods for binary, ordinal, and nominal responses for independent observations and for clustered data with marginal models and random effects models Two new chapters on alternative methods for binary response data, including smoothing and regularization methods, classification methods such as linear discriminant analysis and classification trees, and cluster analysis New sections introducing the Bayesian approach for methods in that chapter More than 100 analyses of data sets and over 600 exercises Notes at the end of each chapter that provide references to recent research and topics not covered in the text, linked to a bibliography of more than 1,200 sources A supplementary website showing how to use R and SAS; for all examples in the text, with information also about SPSS and Stata and with exercise solutions Categorical Data Analysis, Third Edition is an invaluable tool for statisticians and methodologists, such as biostatisticians and researchers in the social and behavioral sciences, medicine and public health, marketing, education, finance, biological and agricultural sciences, and industrial quality control.

Variable Screening Method Using Statistical Sensitivity Analysis in RBDO

Author : Sangjune Bae
Publisher :
ISBN 13 :
Total Pages : 66 pages
Book Rating : 4.:/5 (88 download)

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Book Synopsis Variable Screening Method Using Statistical Sensitivity Analysis in RBDO by : Sangjune Bae

Download or read book Variable Screening Method Using Statistical Sensitivity Analysis in RBDO written by Sangjune Bae and published by . This book was released on 2012 with total page 66 pages. Available in PDF, EPUB and Kindle. Book excerpt: A variable screening method is introduced to reduce the computational cost caused by the curse of dimension of high dimensional problem in RBDO. The screening method considers the output variance of the constraint functions and uses test-of-hypothesis to filter necessary variables. Also, the method is applicable to implicit functions as well as explicit functions. Suitable number of samples to obtain consistent test result is calculated. 3 examples are demonstrated with detailed variable screening procedure and RBDO result.

Developments in Statistical Modelling

Author : Jochen Einbeck
Publisher : Springer Nature
ISBN 13 : 3031657233
Total Pages : 281 pages
Book Rating : 4.0/5 (316 download)

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Book Synopsis Developments in Statistical Modelling by : Jochen Einbeck

Download or read book Developments in Statistical Modelling written by Jochen Einbeck and published by Springer Nature. This book was released on with total page 281 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Random Effect and Latent Variable Model Selection

Author : David Dunson
Publisher : Springer Science & Business Media
ISBN 13 : 0387767215
Total Pages : 174 pages
Book Rating : 4.3/5 (877 download)

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Book Synopsis Random Effect and Latent Variable Model Selection by : David Dunson

Download or read book Random Effect and Latent Variable Model Selection written by David Dunson and published by Springer Science & Business Media. This book was released on 2010-03-18 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt: Random Effect and Latent Variable Model Selection In recent years, there has been a dramatic increase in the collection of multivariate and correlated data in a wide variety of ?elds. For example, it is now standard pr- tice to routinely collect many response variables on each individual in a study. The different variables may correspond to repeated measurements over time, to a battery of surrogates for one or more latent traits, or to multiple types of outcomes having an unknown dependence structure. Hierarchical models that incorporate subje- speci?c parameters are one of the most widely-used tools for analyzing multivariate and correlated data. Such subject-speci?c parameters are commonly referred to as random effects, latent variables or frailties. There are two modeling frameworks that have been particularly widely used as hierarchical generalizations of linear regression models. The ?rst is the linear mixed effects model (Laird and Ware , 1982) and the second is the structural equation model (Bollen , 1989). Linear mixed effects (LME) models extend linear regr- sion to incorporate two components, with the ?rst corresponding to ?xed effects describing the impact of predictors on the mean and the second to random effects characterizing the impact on the covariance. LMEs have also been increasingly used for function estimation. In implementing LME analyses, model selection problems are unavoidable. For example, there may be interest in comparing models with and without a predictor in the ?xed and/or random effects component.