Feature Screening In Ultra High Dimensional Survival Data Analysis

Download Feature Screening In Ultra High Dimensional Survival Data Analysis full books in PDF, epub, and Kindle. Read online Feature Screening In Ultra High Dimensional Survival Data Analysis ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!

Feature Screening in Ultra-high Dimensional Survival Data Analysis

Author : Wei Sun
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (915 download)

DOWNLOAD NOW!

Book Synopsis Feature Screening in Ultra-high Dimensional Survival Data Analysis by : Wei Sun

Download or read book Feature Screening in Ultra-high Dimensional Survival Data Analysis written by Wei Sun and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Much research has been devoted to developing variable selection methods for decades since high dimensional data arise from many scientific and technological fields. Adopting continuous penalties such as the LASSO (Tibshirani, 1996) and the SCAD (Fan and Li, 2001) made it possible to cope with the high dimensionality. Independence screening is very useful tool to identify all the important covariates at less computational cost than the traditional methods when the number of covariates grows at non-polynomial rate of the sample size. When the response is survival time, feature screening is more challenging because the responses are subject to censoring. In this thesis we propose a model-free independence feature screening procedure for ultra-high dimensional survival data. This new procedure can be directly applied for most commonly-used models such as Cox's model, Cox's frailty model, additive Cox's model, parametric, nonparametric and semiparametric proportional odds models and accelerated failure time models, in survival data analysis. Thus, the virtue of the new procedure is desirable since it is usual that little prior information is known for the actual true model for ultra-high dimensional data. The newly proposed procedure is easy to implement and computationally efficient. We systematically studied the theoretical properties of the proposed procedures, and established the sure screening property and consistency in rankingproperty for the proposed procedure. Its performance is evaluated and compared with the existing procedure proposed based on Cox's model (Fan, Feng, & Wu, 2010) by extensive simulation studies and the real data analysis. Since our proposed procedure uses marginal correlation utility measure, an inherent issue is that it cannot identify those important features that are marginally independent withresponse. To resolve this issue, we propose an iterative procedure in spirit similar to iterative sure independent screening procedure proposed by Fan and Lv (2008). The major challenge in the development of the iterative procedure is the lack of definition of residuals under the model-free framework for survival data analysis. The commonly used residuals, such as martingale residual, Schoenfeld residual and deviance residual, are all defined with respect to certain semiparametric models. Therefore those residuals are not applicable in our model-free framework. We instead use the residuals from regressing the entire features space on the previously selected active features. We also carefully studied the performance of the proposed iterative procedures. Our Monte Carlo simulation studies show that the proposediterative procedures performs quite well with moderate sample sizes.

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)

DOWNLOAD NOW!

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:

Procedures for Feature Screening and Interaction Identification in High-dimensional Data Modelling

Author : Ling Zhang
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (111 download)

DOWNLOAD NOW!

Book Synopsis Procedures for Feature Screening and Interaction Identification in High-dimensional Data Modelling by : Ling Zhang

Download or read book Procedures for Feature Screening and Interaction Identification in High-dimensional Data Modelling written by Ling Zhang and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Nowadays, rapid developments in computer technologies have greatly reduced the cost of collecting and storing a massive amount of data. As a result, data with ultrahigh dimensionality begins to enter our vision due to a cheaper cost. It makes new levels of scientific discoveries promising, but also brings us new challenges of analyzing and understanding these data. Variable selection methods, feature screening procedures, and random forest algorithms have been widely used in many scientific fields such as computational biology, health studies, and financial engineering. The goal is to recover the underlying model structure and make an accurate prediction when a large number of predictors are introduced at the initial stage, but only a small subset of them are truly associated with the response.High dimensional survival data analysis is such a scientific field. In the first part of the dissertation, we propose a two-stage feature screening procedure for varying-coefficient Cox model with ultrahigh dimensional covariates. The varying-coefficient model is flexible and powerful for modeling the dynamic effects of coefficients. In the literature, the screening methods for varying-coefficient Cox model are limited to marginal measurements. Distinguished from the marginal screening, the proposed screening procedure is based on the joint partial likelihood of all predictors. Through this, the proposed procedure can effectively identify active predictors that are jointly dependent of, but marginally independent of the response. In order to carry out the proposed procedure, we propose an efficient algorithm and establish the ascent property of the proposed algorithm. We further prove that the proposed procedure possesses the sure screening property: with probability tending to one, the selected variable set includes the actual active predictors. Monte Carlo simulation is conducted to evaluate the finite sample performance of the proposed procedure, with comparison to SIS(Fan and Lv, 2008) procedure and SJS(Yang et al., 2016) for the Cox model. The proposed methodology is also illustrated through the analysis of two real data examples.Although very helpful and computationally efficient, feature screening is not a very powerful method to detect those marginal unimportant variables that participate in high order interaction effects. However, this is the advantage of random forest algorithms because tree structure is a natural and powerful structure for detecting interaction effects. The drawback of the random forest algorithms is that they don't pay enough attention to feature selection, and therefore include lots of redundancy when constructing the forest. This phenomenon will severely influence the interpretability and prediction performance of the forest especially when only a small proportion among a large number of candidate variables are important.In the second part of the dissertation, we propose combining the advantages of forest algorithm and feature screening for a better understanding of the hidden mechanism. To achieve this, we propose a new two-layer random forest algorithm, ``Iteratively Kings' Forests''(iKF), for feature selection and interaction detection in classification and regression problems. In the first layer, we modified the traditional forest constructing process so that we can fully explore the mechanism, both marginal and interaction effects, related to a given important variable(say "King" variable). In the second layer, we iteratively search the next important variable and iterate the process of the first layer for it. Finally, we not only obtain a screened variable index set but also output a short list of ranked highly possible interaction effects. Simulation comparisons are conducted to compare its performance with the feature screening procedure DC-SIS(Li et al., 2012) and random forest algorithm "iRF"(Basu et al., 2018). Also, we apply iKF procedure for empirical analysis to identify important interactions in an early Drosophila embryo data and compare its performance with "iRF".

Feature Screening For Ultra-high Dimensional Longitudinal Data

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

DOWNLOAD NOW!

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.

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)

DOWNLOAD NOW!

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.

Statistical Foundations of Data Science

Author : Jianqing Fan
Publisher : CRC Press
ISBN 13 : 0429527616
Total Pages : 942 pages
Book Rating : 4.4/5 (295 download)

DOWNLOAD NOW!

Book Synopsis Statistical Foundations of Data Science by : Jianqing Fan

Download or read book Statistical Foundations of Data Science written by Jianqing Fan and published by CRC Press. This book was released on 2020-09-21 with total page 942 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Foundations of Data Science gives a thorough introduction to commonly used statistical models, contemporary statistical machine learning techniques and algorithms, along with their mathematical insights and statistical theories. It aims to serve as a graduate-level textbook and a research monograph on high-dimensional statistics, sparsity and covariance learning, machine learning, and statistical inference. It includes ample exercises that involve both theoretical studies as well as empirical applications. The book begins with an introduction to the stylized features of big data and their impacts on statistical analysis. It then introduces multiple linear regression and expands the techniques of model building via nonparametric regression and kernel tricks. It provides a comprehensive account on sparsity explorations and model selections for multiple regression, generalized linear models, quantile regression, robust regression, hazards regression, among others. High-dimensional inference is also thoroughly addressed and so is feature screening. The book also provides a comprehensive account on high-dimensional covariance estimation, learning latent factors and hidden structures, as well as their applications to statistical estimation, inference, prediction and machine learning problems. It also introduces thoroughly statistical machine learning theory and methods for classification, clustering, and prediction. These include CART, random forests, boosting, support vector machines, clustering algorithms, sparse PCA, and deep learning.

Macroeconomic Forecasting in the Era of Big Data

Author : Peter Fuleky
Publisher : Springer Nature
ISBN 13 : 3030311503
Total Pages : 716 pages
Book Rating : 4.0/5 (33 download)

DOWNLOAD NOW!

Book Synopsis Macroeconomic Forecasting in the Era of Big Data by : Peter Fuleky

Download or read book Macroeconomic Forecasting in the Era of Big Data written by Peter Fuleky and published by Springer Nature. This book was released on 2019-11-28 with total page 716 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book surveys big data tools used in macroeconomic forecasting and addresses related econometric issues, including how to capture dynamic relationships among variables; how to select parsimonious models; how to deal with model uncertainty, instability, non-stationarity, and mixed frequency data; and how to evaluate forecasts, among others. Each chapter is self-contained with references, and provides solid background information, while also reviewing the latest advances in the field. Accordingly, the book offers a valuable resource for researchers, professional forecasters, and students of quantitative economics.

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)

DOWNLOAD NOW!

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.

Advances and Innovations in Statistics and Data Science

Author : Wenqing He
Publisher : Springer Nature
ISBN 13 : 3031083296
Total Pages : 339 pages
Book Rating : 4.0/5 (31 download)

DOWNLOAD NOW!

Book Synopsis Advances and Innovations in Statistics and Data Science by : Wenqing He

Download or read book Advances and Innovations in Statistics and Data Science written by Wenqing He and published by Springer Nature. This book was released on 2022-10-27 with total page 339 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book highlights selected papers from the 4th ICSA-Canada Chapter Symposium, as well as invited articles from established researchers in the areas of statistics and data science. It covers a variety of topics, including methodology development in data science, such as methodology in the analysis of high dimensional data, feature screening in ultra-high dimensional data and natural language ranking; statistical analysis challenges in sampling, multivariate survival models and contaminated data, as well as applications of statistical methods. With this book, readers can make use of frontier research methods to tackle their problems in research, education, training and consultation.

Statistical Foundations of Data Science

Author : Jianqing Fan
Publisher : CRC Press
ISBN 13 : 1466510854
Total Pages : 752 pages
Book Rating : 4.4/5 (665 download)

DOWNLOAD NOW!

Book Synopsis Statistical Foundations of Data Science by : Jianqing Fan

Download or read book Statistical Foundations of Data Science written by Jianqing Fan and published by CRC Press. This book was released on 2020-09-21 with total page 752 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Foundations of Data Science gives a thorough introduction to commonly used statistical models, contemporary statistical machine learning techniques and algorithms, along with their mathematical insights and statistical theories. It aims to serve as a graduate-level textbook and a research monograph on high-dimensional statistics, sparsity and covariance learning, machine learning, and statistical inference. It includes ample exercises that involve both theoretical studies as well as empirical applications. The book begins with an introduction to the stylized features of big data and their impacts on statistical analysis. It then introduces multiple linear regression and expands the techniques of model building via nonparametric regression and kernel tricks. It provides a comprehensive account on sparsity explorations and model selections for multiple regression, generalized linear models, quantile regression, robust regression, hazards regression, among others. High-dimensional inference is also thoroughly addressed and so is feature screening. The book also provides a comprehensive account on high-dimensional covariance estimation, learning latent factors and hidden structures, as well as their applications to statistical estimation, inference, prediction and machine learning problems. It also introduces thoroughly statistical machine learning theory and methods for classification, clustering, and prediction. These include CART, random forests, boosting, support vector machines, clustering algorithms, sparse PCA, and deep learning.

Volume 16: How to Detect and Handle Outliers

Author : Boris Iglewicz
Publisher : Quality Press
ISBN 13 : 0873892607
Total Pages : 99 pages
Book Rating : 4.8/5 (738 download)

DOWNLOAD NOW!

Book Synopsis Volume 16: How to Detect and Handle Outliers by : Boris Iglewicz

Download or read book Volume 16: How to Detect and Handle Outliers written by Boris Iglewicz and published by Quality Press. This book was released on 1993-01-08 with total page 99 pages. Available in PDF, EPUB and Kindle. Book excerpt: Outliers are the key focus of this book. The authors concentrate on the practical aspects of dealing with outliers in the forms of data that arise most often in applications: single and multiple samples, linear regression, and factorial experiments. Available only as an E-Book.

Introduction to Empirical Processes and Semiparametric Inference

Author : Michael R. Kosorok
Publisher : Springer Science & Business Media
ISBN 13 : 0387749780
Total Pages : 482 pages
Book Rating : 4.3/5 (877 download)

DOWNLOAD NOW!

Book Synopsis Introduction to Empirical Processes and Semiparametric Inference by : Michael R. Kosorok

Download or read book Introduction to Empirical Processes and Semiparametric Inference written by Michael R. Kosorok and published by Springer Science & Business Media. This book was released on 2007-12-29 with total page 482 pages. Available in PDF, EPUB and Kindle. Book excerpt: Kosorok’s brilliant text provides a self-contained introduction to empirical processes and semiparametric inference. These powerful research techniques are surprisingly useful for developing methods of statistical inference for complex models and in understanding the properties of such methods. This is an authoritative text that covers all the bases, and also a friendly and gradual introduction to the area. The book can be used as research reference and textbook.

Quantile Regression

Author : Cristina Davino
Publisher : John Wiley & Sons
ISBN 13 : 111997528X
Total Pages : 288 pages
Book Rating : 4.1/5 (199 download)

DOWNLOAD NOW!

Book Synopsis Quantile Regression by : Cristina Davino

Download or read book Quantile Regression written by Cristina Davino and published by John Wiley & Sons. This book was released on 2013-12-31 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: A guide to the implementation and interpretation of Quantile Regression models This book explores the theory and numerous applications of quantile regression, offering empirical data analysis as well as the software tools to implement the methods. The main focus of this book is to provide the reader with a comprehensive description of the main issues concerning quantile regression; these include basic modeling, geometrical interpretation, estimation and inference for quantile regression, as well as issues on validity of the model, diagnostic tools. Each methodological aspect is explored and followed by applications using real data. Quantile Regression: Presents a complete treatment of quantile regression methods, including, estimation, inference issues and application of methods. Delivers a balance between methodolgy and application Offers an overview of the recent developments in the quantile regression framework and why to use quantile regression in a variety of areas such as economics, finance and computing. Features a supporting website (www.wiley.com/go/quantile_regression) hosting datasets along with R, Stata and SAS software code. Researchers and PhD students in the field of statistics, economics, econometrics, social and environmental science and chemistry will benefit from this book.

A New Variable Screening Procedure for COX'S Model

Author : Ye Yu
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (94 download)

DOWNLOAD NOW!

Book Synopsis A New Variable Screening Procedure for COX'S Model by : Ye Yu

Download or read book A New Variable Screening Procedure for COX'S Model written by Ye Yu and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Survival data with ultrahigh dimensional covariates such as genetic markers have been collected in medical studies and other fields. In this thesis, we propose a feature screening procedure for the Cox model with ultrahigh dimensional covariates. The proposed procedure is distinguished from the existing sure independence screening (SIS) procedures (Fan, Feng and Wu, 2010, Zhao and Li, 2012) in that the proposed procedure is based on joint likelihood of potential active predictors, and therefore is not a marginal screening procedure.The proposed procedure can effectively identify active predictors that are jointly dependent but marginally independent of the response without performing aniterative procedure. We develop a computationally effective algorithm to carry out the proposed procedure and establish the ascent property of the proposed algorithm. We also conduct Monte Carlo simulation to evaluate the finite sample performance of the proposed procedure and further compare the proposed procedureand existing SIS procedures. The proposed methodology is also demonstrated through an empirical analysis of a real data example.

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)

DOWNLOAD NOW!

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)

Symmetric Multivariate and Related Distributions

Author : Kai Wang Fang
Publisher : CRC Press
ISBN 13 : 1351093940
Total Pages : 165 pages
Book Rating : 4.3/5 (51 download)

DOWNLOAD NOW!

Book Synopsis Symmetric Multivariate and Related Distributions by : Kai Wang Fang

Download or read book Symmetric Multivariate and Related Distributions written by Kai Wang Fang and published by CRC Press. This book was released on 2018-01-18 with total page 165 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since the publication of the by now classical Johnson and Kotz Continuous Multivariate Distributions (Wiley, 1972) there have been substantial developments in multivariate distribution theory especially in the area of non-normal symmetric multivariate distributions. The book by Fang, Kotz and Ng summarizes these developments in a manner which is accessible to a reader with only limited background (advanced real-analysis calculus, linear algebra and elementary matrix calculus). Many of the results in this field are due to Kai-Tai Fang and his associates and appeared in Chinese publications only. A thorough literature search was conducted and the book represents the latest work - as of 1988 - in this rapidly developing field of multivariate distributions. The authors are experts in statistical distribution theory.

Statistical Analysis with Measurement Error or Misclassification

Author : Grace Y. Yi
Publisher : Springer
ISBN 13 : 1493966405
Total Pages : 497 pages
Book Rating : 4.4/5 (939 download)

DOWNLOAD NOW!

Book Synopsis Statistical Analysis with Measurement Error or Misclassification by : Grace Y. Yi

Download or read book Statistical Analysis with Measurement Error or Misclassification written by Grace Y. Yi and published by Springer. This book was released on 2017-08-02 with total page 497 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph on measurement error and misclassification covers a broad range of problems and emphasizes unique features in modeling and analyzing problems arising from medical research and epidemiological studies. Many measurement error and misclassification problems have been addressed in various fields over the years as well as with a wide spectrum of data, including event history data (such as survival data and recurrent event data), correlated data (such as longitudinal data and clustered data), multi-state event data, and data arising from case-control studies. Statistical Analysis with Measurement Error or Misclassification: Strategy, Method and Application brings together assorted methods in a single text and provides an update of recent developments for a variety of settings. Measurement error effects and strategies of handling mismeasurement for different models are closely examined in combination with applications to specific problems. Readers with diverse backgrounds and objectives can utilize this text. Familiarity with inference methods—such as likelihood and estimating function theory—or modeling schemes in varying settings—such as survival analysis and longitudinal data analysis—can result in a full appreciation of the material, but it is not essential since each chapter provides basic inference frameworks and background information on an individual topic to ease the access of the material. The text is presented in a coherent and self-contained manner and highlights the essence of commonly used modeling and inference methods. This text can serve as a reference book for researchers interested in statistical methodology for handling data with measurement error or misclassification; as a textbook for graduate students, especially for those majoring in statistics and biostatistics; or as a book for applied statisticians whose interest focuses on analysis of error-contaminated data. Grace Y. Yi is Professor of Statistics and University Research Chair at the University of Waterloo. She is the 2010 winner of the CRM-SSC Prize, an honor awarded in recognition of a statistical scientist's professional accomplishments in research during the first 15 years after having received a doctorate. She is a Fellow of the American Statistical Association and an Elected Member of the International Statistical Institute.