Penalized Likelihood Methods For Sparse Datasets With Applications To Genetic Epidemiology

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Penalized Likelihood Methods for Sparse Datasets, with Applications to Genetic Epidemiology

Author : Ying Yu
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (14 download)

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Book Synopsis Penalized Likelihood Methods for Sparse Datasets, with Applications to Genetic Epidemiology by : Ying Yu

Download or read book Penalized Likelihood Methods for Sparse Datasets, with Applications to Genetic Epidemiology written by Ying Yu and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Increasingly, logistic regression methods for genetic association studies of binary phenotypes must be able to accommodate data sparsity, which arises from unbalanced case-control ratios and/or rare exposures. Sparseness leads to maximum likelihood estimates (MLEs) of log odds-ratio parameters that are biased away from their null value of zero and tests with inflated type I errors. Different penalized-likelihood methods have been developed to mitigate sparse-data bias. We study penalized logistic and conditional regression using a class of log-F priors indexed by a shrinkage parameter m to shrink the biased MLE towards zero. The thesis is organized in three parts. First, we propose a two-step methodology for implementing log-F penalization for inference of regression parameters from logistic regression, with application to genome-wide association studies. In the first step we estimate the shrinkage parameter, and in the second step we use the penalized regression estimator to estimate single-variant associations across the genome. Next, we explore log-F penalization for inference of regression parameters from conditional logistic regression, with application to data from matched case-control and case-parent trio studies. In the first two projects we use simulation to study the statistical properties of our methods and make comparisons to methods that use Firth penalization. Finally, we apply log-F-penalized logistic regression to data from the UK Biobank, to investigate the method's feasibility for genome-wide, biobank-scale data. The complexity and size of biobank data present unique challenges, and we make modifications to our methodology to increase its flexibility and adaptability to such datasets.

Applications of Penalized Likelihood Methods for Feature Selection in Statistical Modeling

Author : Chen Xu
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (13 download)

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Book Synopsis Applications of Penalized Likelihood Methods for Feature Selection in Statistical Modeling by : Chen Xu

Download or read book Applications of Penalized Likelihood Methods for Feature Selection in Statistical Modeling written by Chen Xu and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Sparse Simultaneous Penalized Variable Selection in Hierarchical Structure Genetic Data Analysis

Author : Shuang Huang
Publisher :
ISBN 13 :
Total Pages : 101 pages
Book Rating : 4.:/5 (11 download)

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Book Synopsis Sparse Simultaneous Penalized Variable Selection in Hierarchical Structure Genetic Data Analysis by : Shuang Huang

Download or read book Sparse Simultaneous Penalized Variable Selection in Hierarchical Structure Genetic Data Analysis written by Shuang Huang and published by . This book was released on 2017 with total page 101 pages. Available in PDF, EPUB and Kindle. Book excerpt: The sparse simultaneous penalized variable selection method for data with hierarchical structure is proposed to identify the quantitative trait loci and expression traits that are related to certain clinical trait in genetic data analysis. This method is developed for data sets in which the dependency is linear, and among a large number of gene loci and expression traits candidates, relatively few are important to the interested clinical trait. The method focuses on identifying the candidates in genome set and expression traits that are significantly related to clinical observation via the hierarchical dependence structure. A penalized linear model is used to reduce the number of parameters, using a novel computational algorithm that can handle the unknowns simultaneously. A data-adaptive tuning procedure based on cross validation acts as a parameter selector. Simulation studies are conducted to check the performance of the proposed method, and to compare with some well developed methods, including several penalized methods and Step AIC method. The real data application is done on a data set from an obesity study. The data set contains 541 mice, and for each individual, over 1,000 expression traits and around 1,000 gene loci are recorded. We compare the finding of our method with previous studies on the same species of mice and the similarity and difference of the outcomes are discussed.

Big Data in Radiation Oncology

Author : Jun Deng
Publisher : CRC Press
ISBN 13 : 1351801120
Total Pages : 289 pages
Book Rating : 4.3/5 (518 download)

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Book Synopsis Big Data in Radiation Oncology by : Jun Deng

Download or read book Big Data in Radiation Oncology written by Jun Deng and published by CRC Press. This book was released on 2019-03-07 with total page 289 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data in Radiation Oncology gives readers an in-depth look into how big data is having an impact on the clinical care of cancer patients. While basic principles and key analytical and processing techniques are introduced in the early chapters, the rest of the book turns to clinical applications, in particular for cancer registries, informatics, radiomics, radiogenomics, patient safety and quality of care, patient-reported outcomes, comparative effectiveness, treatment planning, and clinical decision-making. More features of the book are: Offers the first focused treatment of the role of big data in the clinic and its impact on radiation therapy. Covers applications in cancer registry, radiomics, patient safety, quality of care, treatment planning, decision making, and other key areas. Discusses the fundamental principles and techniques for processing and analysis of big data. Address the use of big data in cancer prevention, detection, prognosis, and management. Provides practical guidance on implementation for clinicians and other stakeholders. Dr. Jun Deng is a professor at the Department of Therapeutic Radiology of Yale University School of Medicine and an ABR board certified medical physicist at Yale-New Haven Hospital. He has received numerous honors and awards such as Fellow of Institute of Physics in 2004, AAPM Medical Physics Travel Grant in 2008, ASTRO IGRT Symposium Travel Grant in 2009, AAPM-IPEM Medical Physics Travel Grant in 2011, and Fellow of AAPM in 2013. Lei Xing, Ph.D., is the Jacob Haimson Professor of Medical Physics and Director of Medical Physics Division of Radiation Oncology Department at Stanford University. His research has been focused on inverse treatment planning, tomographic image reconstruction, CT, optical and PET imaging instrumentations, image guided interventions, nanomedicine, and applications of molecular imaging in radiation oncology. Dr. Xing is on the editorial boards of a number of journals in radiation physics and medical imaging, and is recipient of numerous awards, including the American Cancer Society Research Scholar Award, The Whitaker Foundation Grant Award, and a Max Planck Institute Fellowship.

Bayesian Data Analysis, Third Edition

Author : Andrew Gelman
Publisher : CRC Press
ISBN 13 : 1439840954
Total Pages : 677 pages
Book Rating : 4.4/5 (398 download)

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Book Synopsis Bayesian Data Analysis, Third Edition by : Andrew Gelman

Download or read book Bayesian Data Analysis, Third Edition written by Andrew Gelman and published by CRC Press. This book was released on 2013-11-01 with total page 677 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.

Information Criteria and Statistical Modeling

Author : Sadanori Konishi
Publisher : Springer Science & Business Media
ISBN 13 : 0387718869
Total Pages : 282 pages
Book Rating : 4.3/5 (877 download)

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Book Synopsis Information Criteria and Statistical Modeling by : Sadanori Konishi

Download or read book Information Criteria and Statistical Modeling written by Sadanori Konishi and published by Springer Science & Business Media. This book was released on 2008 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical modeling is a critical tool in scientific research. This book provides comprehensive explanations of the concepts and philosophy of statistical modeling, together with a wide range of practical and numerical examples. The authors expect this work to be of great value not just to statisticians but also to researchers and practitioners in various fields of research such as information science, computer science, engineering, bioinformatics, economics, marketing and environmental science. It’s a crucial area of study, as statistical models are used to understand phenomena with uncertainty and to determine the structure of complex systems. They’re also used to control such systems, as well as to make reliable predictions in various natural and social science fields.

Elements of Causal Inference

Author : Jonas Peters
Publisher : MIT Press
ISBN 13 : 0262037319
Total Pages : 289 pages
Book Rating : 4.2/5 (62 download)

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Book Synopsis Elements of Causal Inference by : Jonas Peters

Download or read book Elements of Causal Inference written by Jonas Peters and published by MIT Press. This book was released on 2017-11-29 with total page 289 pages. Available in PDF, EPUB and Kindle. Book excerpt: A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning. The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data. After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem. The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts.

Bayesian Networks

Author : Olivier Pourret
Publisher : John Wiley & Sons
ISBN 13 : 9780470994542
Total Pages : 446 pages
Book Rating : 4.9/5 (945 download)

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Book Synopsis Bayesian Networks by : Olivier Pourret

Download or read book Bayesian Networks written by Olivier Pourret and published by John Wiley & Sons. This book was released on 2008-04-30 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Networks, the result of the convergence of artificial intelligence with statistics, are growing in popularity. Their versatility and modelling power is now employed across a variety of fields for the purposes of analysis, simulation, prediction and diagnosis. This book provides a general introduction to Bayesian networks, defining and illustrating the basic concepts with pedagogical examples and twenty real-life case studies drawn from a range of fields including medicine, computing, natural sciences and engineering. Designed to help analysts, engineers, scientists and professionals taking part in complex decision processes to successfully implement Bayesian networks, this book equips readers with proven methods to generate, calibrate, evaluate and validate Bayesian networks. The book: Provides the tools to overcome common practical challenges such as the treatment of missing input data, interaction with experts and decision makers, determination of the optimal granularity and size of the model. Highlights the strengths of Bayesian networks whilst also presenting a discussion of their limitations. Compares Bayesian networks with other modelling techniques such as neural networks, fuzzy logic and fault trees. Describes, for ease of comparison, the main features of the major Bayesian network software packages: Netica, Hugin, Elvira and Discoverer, from the point of view of the user. Offers a historical perspective on the subject and analyses future directions for research. Written by leading experts with practical experience of applying Bayesian networks in finance, banking, medicine, robotics, civil engineering, geology, geography, genetics, forensic science, ecology, and industry, the book has much to offer both practitioners and researchers involved in statistical analysis or modelling in any of these fields.

Cure Models

Author : Yingwei Peng
Publisher : CRC Press
ISBN 13 : 0429629680
Total Pages : 268 pages
Book Rating : 4.4/5 (296 download)

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Book Synopsis Cure Models by : Yingwei Peng

Download or read book Cure Models written by Yingwei Peng and published by CRC Press. This book was released on 2021-03-22 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cure Models: Methods, Applications and Implementation is the first book in the last 25 years that provides a comprehensive and systematic introduction to the basics of modern cure models, including estimation, inference, and software. This book is useful for statistical researchers and graduate students, and practitioners in other disciplines to have a thorough review of modern cure model methodology and to seek appropriate cure models in applications. The prerequisites of this book include some basic knowledge of statistical modeling, survival models, and R and SAS for data analysis. The book features real-world examples from clinical trials and population-based studies and a detailed introduction to R packages, SAS macros, and WinBUGS programs to fit some cure models. The main topics covered include the foundation of statistical estimation and inference of cure models for independent and right-censored survival data, cure modeling for multivariate, recurrent-event, and competing-risks survival data, and joint modeling with longitudinal data, statistical testing for the existence and difference of cure rates and sufficient follow-up, new developments in Bayesian cure models, applications of cure models in public health research and clinical trials.

Big and Complex Data Analysis

Author : S. Ejaz Ahmed
Publisher : Springer
ISBN 13 : 3319415735
Total Pages : 390 pages
Book Rating : 4.3/5 (194 download)

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Book Synopsis Big and Complex Data Analysis by : S. Ejaz Ahmed

Download or read book Big and Complex Data Analysis written by S. Ejaz Ahmed and published by Springer. This book was released on 2017-03-21 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume conveys some of the surprises, puzzles and success stories in high-dimensional and complex data analysis and related fields. Its peer-reviewed contributions showcase recent advances in variable selection, estimation and prediction strategies for a host of useful models, as well as essential new developments in the field. The continued and rapid advancement of modern technology now allows scientists to collect data of increasingly unprecedented size and complexity. Examples include epigenomic data, genomic data, proteomic data, high-resolution image data, high-frequency financial data, functional and longitudinal data, and network data. Simultaneous variable selection and estimation is one of the key statistical problems involved in analyzing such big and complex data. The purpose of this book is to stimulate research and foster interaction between researchers in the area of high-dimensional data analysis. More concretely, its goals are to: 1) highlight and expand the breadth of existing methods in big data and high-dimensional data analysis and their potential for the advancement of both the mathematical and statistical sciences; 2) identify important directions for future research in the theory of regularization methods, in algorithmic development, and in methodologies for different application areas; and 3) facilitate collaboration between theoretical and subject-specific researchers.

Modeling Discrete Time-to-Event Data

Author : Gerhard Tutz
Publisher : Springer
ISBN 13 : 3319281585
Total Pages : 252 pages
Book Rating : 4.3/5 (192 download)

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Book Synopsis Modeling Discrete Time-to-Event Data by : Gerhard Tutz

Download or read book Modeling Discrete Time-to-Event Data written by Gerhard Tutz and published by Springer. This book was released on 2016-06-14 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on statistical methods for the analysis of discrete failure times. Failure time analysis is one of the most important fields in statistical research, with applications affecting a wide range of disciplines, in particular, demography, econometrics, epidemiology and clinical research. Although there are a large variety of statistical methods for failure time analysis, many techniques are designed for failure times that are measured on a continuous scale. In empirical studies, however, failure times are often discrete, either because they have been measured in intervals (e.g., quarterly or yearly) or because they have been rounded or grouped. The book covers well-established methods like life-table analysis and discrete hazard regression models, but also introduces state-of-the art techniques for model evaluation, nonparametric estimation and variable selection. Throughout, the methods are illustrated by real life applications, and relationships to survival analysis in continuous time are explained. Each section includes a set of exercises on the respective topics. Various functions and tools for the analysis of discrete survival data are collected in the R package discSurv that accompanies the book.

Machine Learning Refined

Author : Jeremy Watt
Publisher : Cambridge University Press
ISBN 13 : 1108480721
Total Pages : 597 pages
Book Rating : 4.1/5 (84 download)

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Book Synopsis Machine Learning Refined by : Jeremy Watt

Download or read book Machine Learning Refined written by Jeremy Watt and published by Cambridge University Press. This book was released on 2020-01-09 with total page 597 pages. Available in PDF, EPUB and Kindle. Book excerpt: An intuitive approach to machine learning covering key concepts, real-world applications, and practical Python coding exercises.

Automated Machine Learning

Author : Frank Hutter
Publisher : Springer
ISBN 13 : 3030053180
Total Pages : 223 pages
Book Rating : 4.0/5 (3 download)

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Book Synopsis Automated Machine Learning by : Frank Hutter

Download or read book Automated Machine Learning written by Frank Hutter and published by Springer. This book was released on 2019-05-17 with total page 223 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.

Generalized Linear Models

Author : P. McCullagh
Publisher : Routledge
ISBN 13 : 1351445847
Total Pages : 361 pages
Book Rating : 4.3/5 (514 download)

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Book Synopsis Generalized Linear Models by : P. McCullagh

Download or read book Generalized Linear Models written by P. McCullagh and published by Routledge. This book was released on 2019-01-22 with total page 361 pages. Available in PDF, EPUB and Kindle. Book excerpt: The success of the first edition of Generalized Linear Models led to the updated Second Edition, which continues to provide a definitive unified, treatment of methods for the analysis of diverse types of data. Today, it remains popular for its clarity, richness of content and direct relevance to agricultural, biological, health, engineering, and ot

Cluster Analysis

Author : Brian S. Everitt
Publisher :
ISBN 13 :
Total Pages : 122 pages
Book Rating : 4.:/5 (878 download)

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Book Synopsis Cluster Analysis by : Brian S. Everitt

Download or read book Cluster Analysis written by Brian S. Everitt and published by . This book was released on 1977 with total page 122 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Ecological Models and Data in R

Author : Benjamin M. Bolker
Publisher : Princeton University Press
ISBN 13 : 0691125228
Total Pages : 408 pages
Book Rating : 4.6/5 (911 download)

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Book Synopsis Ecological Models and Data in R by : Benjamin M. Bolker

Download or read book Ecological Models and Data in R written by Benjamin M. Bolker and published by Princeton University Press. This book was released on 2008-07-21 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction and background; Exploratory data analysis and graphics; Deterministic functions for ecological modeling; Probability and stochastic distributions for ecological modeling; Stochatsic simulation and power analysis; Likelihood and all that; Optimization and all that; Likelihood examples; Standar statistics revisited; Modeling variance; Dynamic models.

Age-Period-Cohort Analysis

Author : Yang Yang
Publisher : CRC Press
ISBN 13 : 1466507535
Total Pages : 352 pages
Book Rating : 4.4/5 (665 download)

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Book Synopsis Age-Period-Cohort Analysis by : Yang Yang

Download or read book Age-Period-Cohort Analysis written by Yang Yang and published by CRC Press. This book was released on 2016-04-19 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores the ways in which statistical models, methods, and research designs can be used to open new possibilities for APC analysis. Within a single, consistent HAPC-GLMM statistical modeling framework, the authors synthesize APC models and methods for three research designs: age-by-time period tables of population rates or proportions, repeated cross-section sample surveys, and accelerated longitudinal panel studies. They show how the empirical application of the models to various problems leads to many fascinating findings on how outcome variables develop along the age, period, and cohort dimensions.