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Inference Of Nonparametric Hypothesis Testing On High Dimensional Longitudinal Data And Its Application In Dna Copy Number Variation And Micro Array Data Analysis
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Book Synopsis Inference of Nonparametric Hypothesis Testing on High Dimensional Longitudinal Data and Its Application in DNA Copy Number Variation and Micro Array Data Analysis by : Ke Zhang
Download or read book Inference of Nonparametric Hypothesis Testing on High Dimensional Longitudinal Data and Its Application in DNA Copy Number Variation and Micro Array Data Analysis written by Ke Zhang and published by . This book was released on 2008 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: High throughput screening technologies have generated a huge amount of biological data in the last ten years. With the easy availability of array technology, researchers started to investigate biological mechanisms using experiments with more sophisticated designs that pose novel challenges to statistical analysis. We provide theory for robust statistical tests in three flexible models. In the first model, we consider the hypothesis testing problems when there are a large number of variables observed repeatedly over time. A potential application is in tumor genomics where an array comparative genome hybridization (aCGH) study will be used to detect progressive DNA copy number changes in tumor development. In the second model, we consider hypothesis testing theory in a longitudinal microarray study when there are multiple treatments or experimental conditions. The tests developed can be used to detect treatment effects for a large group of genes and discover genes that respond to treatment over time. In the third model, we address a hypothesis testing problem that could arise when array data from different sources are to be integrated. We perform statistical tests by assuming a nested design. In all models, robust test statistics were constructed based on moment methods allowing unbalanced design and arbitrary heteroscedasticity. The limiting distributions were derived under the nonclassical setting when the number of probes is large. The test statistics are not targeted at a single probe. Instead, we are interested in testing for a selected set of probes simultaneously. Simulation studies were carried out to compare the proposed methods with some traditional tests using linear mixed-effects models and generalized estimating equations. Interesting results obtained with the proposed theory in two cancer genomic studies suggest that the new methods are promising for a wide range of biological applications with longitudinal arrays.
Book Synopsis Nonparametric Inference for High Dimensional Data by : Subhadeep Mukhopadhyay
Download or read book Nonparametric Inference for High Dimensional Data written by Subhadeep Mukhopadhyay and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Learning from data, especially 'Big Data', is becoming increasingly popular under names such as Data Mining, Data Science, Machine Learning, Statistical Learning and High Dimensional Data Analysis. In this dissertation we propose a new related field, which we call 'United Nonparametric Data Science' - applied statistics with "just in time" theory. It integrates the practice of traditional and novel statistical methods for nonparametric exploratory data modeling, and it is applicable to teaching introductory statistics courses that are closer to modern frontiers of scientific research. Our framework includes small data analysis (combining traditional and modern nonparametric statistical inference), big and high dimensional data analysis (by statistical modeling methods that extend our unified framework for small data analysis). The first part of the dissertation (Chapters 2 and 3) has been oriented by the goal of developing a new theoretical foundation to unify many cultures of statistical science and statistical learning methods using mid-distribution function, custom made orthonormal score function, comparison density, copula density, LP moments and comoments. It is also examined how this elegant theory yields solution to many important applied problems. In the second part (Chapter 4) we extend the traditional empirical likelihood (EL), a versatile tool for nonparametric inference, in the high dimensional context. We introduce a modified version of the EL method that is computationally simpler and applicable to a large class of "large p small n" problems, allowing p to grow faster than n. This is an important step in generalizing the EL in high dimensions beyond the p ≤ n threshold where the standard EL and its existing variants fail. We also present detailed theoretical study of the proposed method. The electronic version of this dissertation is accessible from http://hdl.handle.net/1969.1/149430
Book Synopsis Contributions to Ancestral Inference for Supercritical Banching Processes and High-dimensional Data Analysis by : Bret Michael Hanlon
Download or read book Contributions to Ancestral Inference for Supercritical Banching Processes and High-dimensional Data Analysis written by Bret Michael Hanlon and published by . This book was released on 2009 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis is concerned with statistical methods that are relevant in the scientific study of gene expression data. It is customary in these areas to use microarray technology as a first step in identifying the genes that are differentially expressed followed by using quantitative polymerase chain reaction (qPCR) as a confirmatory tool. The first part of thesis addresses statistical analysis for qPCR data, while the second part of the thesis addresses the so-called large p, small n problem, using microarray gene expression data as the motivating example. Description of the gene expression profiles from PCR can be cast within the more general framework of ancestral inference for branching processes. Accordingly, part one of the thesis is devoted to the study of branching processes initiated by a random number of ancestors. We address issues concerning modeling, inference, and asymptotic justification of the proposed methodologies. The second part of the thesis focuses on microarray data, specifically developing multivariate techniques for identifying differentially expressed genes. The results can be viewed in the more general context of multiple hypothesis testing or the multivariate testing problem.
Book Synopsis Methods and Applications of Longitudinal Data Analysis by : Xian Liu
Download or read book Methods and Applications of Longitudinal Data Analysis written by Xian Liu and published by Elsevier. This book was released on 2015-09-01 with total page 531 pages. Available in PDF, EPUB and Kindle. Book excerpt: Methods and Applications of Longitudinal Data Analysis describes methods for the analysis of longitudinal data in the medical, biological and behavioral sciences. It introduces basic concepts and functions including a variety of regression models, and their practical applications across many areas of research. Statistical procedures featured within the text include: descriptive methods for delineating trends over time linear mixed regression models with both fixed and random effects covariance pattern models on correlated errors generalized estimating equations nonlinear regression models for categorical repeated measurements techniques for analyzing longitudinal data with non-ignorable missing observations Emphasis is given to applications of these methods, using substantial empirical illustrations, designed to help users of statistics better analyze and understand longitudinal data. Methods and Applications of Longitudinal Data Analysis equips both graduate students and professionals to confidently apply longitudinal data analysis to their particular discipline. It also provides a valuable reference source for applied statisticians, demographers and other quantitative methodologists. From novice to professional: this book starts with the introduction of basic models and ends with the description of some of the most advanced models in longitudinal data analysis Enables students to select the correct statistical methods to apply to their longitudinal data and avoid the pitfalls associated with incorrect selection Identifies the limitations of classical repeated measures models and describes newly developed techniques, along with real-world examples.
Book Synopsis Evaluation of Information in Longitudinal Data by : Max Petzold
Download or read book Evaluation of Information in Longitudinal Data written by Max Petzold and published by . This book was released on 2003 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This is a Ph.D. dissertation. Longitudinal analysis is important when individual effects are present. In this thesis some methods for different kinds of inference on longitudinal data are derived and evaluated in small sample settings. Appropriate evaluation criteria are determined from the actual inferential situation. It is based on three papers: ""Maximum Likelihood Ratio Based Small-Sample Tests for Random Coefficients in Linear Regression"", ""Preliminary Testing in a Class of Simple Non-Linear Mixed Models to Improve Estimation Accuracy"" and ""Detection of Intra-Uterine Growth Restriction""."
Book Synopsis Longitudinal Data Analysis by : Garrett Fitzmaurice
Download or read book Longitudinal Data Analysis written by Garrett Fitzmaurice and published by CRC Press. This book was released on 2008-08-11 with total page 633 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although many books currently available describe statistical models and methods for analyzing longitudinal data, they do not highlight connections between various research threads in the statistical literature. Responding to this void, Longitudinal Data Analysis provides a clear, comprehensive, and unified overview of state-of-the-art theory
Book Synopsis Analysis of Longitudinal Data with Example by : You-Gan Wang
Download or read book Analysis of Longitudinal Data with Example written by You-Gan Wang and published by CRC Press. This book was released on 2022-01-28 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: Development in methodology on longitudinal data is fast. Currently, there are a lack of intermediate /advanced level textbooks which introduce students and practicing statisticians to the updated methods on correlated data inference. This book will present a discussion of the modern approaches to inference, including the links between the theories of estimators and various types of efficient statistical models including likelihood-based approaches. The theory will be supported with practical examples of R-codes and R-packages applied to interesting case-studies from a number of different areas. Key Features: •Includes the most up-to-date methods •Use simple examples to demonstrate complex methods •Uses real data from a number of areas •Examples utilize R code
Book Synopsis Practical Longitudinal Data Analysis by : David J. Hand
Download or read book Practical Longitudinal Data Analysis written by David J. Hand and published by Routledge. This book was released on 2017-10-06 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text describes regression-based approaches to analyzing longitudinal and repeated measures data. It emphasizes statistical models, discusses the relationships between different approaches, and uses real data to illustrate practical applications. It uses commercially available software when it exists and illustrates the program code and output. The data appendix provides many real data sets-beyond those used for the examples-which can serve as the basis for exercises.
Book Synopsis Predictive Modeling for High Dimensional Longitudinal Data by : Junjie Liang
Download or read book Predictive Modeling for High Dimensional Longitudinal Data written by Junjie Liang and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Longitudinal studies, which involve repeated observations, taken at irregularly spaced time points, for a set of individuals over time, are ubiquitous in many applications. Predictive models for longitudinal data generally need to take into account the data correlation, i.e., correlation among repeated observations of the individual and/or correlation among groups of individuals. Ignoring either part of the correlation can lead to misleading statistical inferences. It can be non-trivial to choose a suitable correlation structure that reflects the correlations present in the data. The relationships between the variables and outcomes of interest can be highly complex and non-linear. Furthermore, modern applications often call for longitudinal methods that scale gracefully with increasing number of variables and millions of data points. The target for this dissertation is to address the challenges in longitudinal data analysis using machine learning and representation learning approaches. Specifically, our work is dedicated to redesign the state-of-the-art longitudinal models to fit in the large-scale, high-dimensional longitudinal settings. We focus on improving the mixed effects models and non-parametric models by answering the following research questions: (i) How can we design mixed effects models to handle longitudinal data with thousands of variables and automate the selection between fixed and random effects? (ii) How can we design non-parametric models to handle longitudinal data with time-varying and time-invariant effects and automate the discovery of complex correlation? (iii) How can we design non-parametric models to handle longitudinal data with outcomes that could show state transitions, abrupt discontinuities and complex correlation? Against this background, this dissertation investigates two lines of approaches, Factorization Machines and Gaussian Process. We tackle both the theoretical and practical challenges in adapting these approaches to longitudinal settings. For each proposed model, we explore provably efficient algorithm to improve its applicability over high-dimensional data.
Book Synopsis Analysis of Longitudinal Data with Example by : You-Gan Wang
Download or read book Analysis of Longitudinal Data with Example written by You-Gan Wang and published by CRC Press. This book was released on 2022-01-28 with total page 213 pages. Available in PDF, EPUB and Kindle. Book excerpt: Development in methodology on longitudinal data is fast. Currently, there are a lack of intermediate /advanced level textbooks which introduce students and practicing statisticians to the updated methods on correlated data inference. This book will present a discussion of the modern approaches to inference, including the links between the theories of estimators and various types of efficient statistical models including likelihood-based approaches. The theory will be supported with practical examples of R-codes and R-packages applied to interesting case-studies from a number of different areas. Key Features: •Includes the most up-to-date methods •Use simple examples to demonstrate complex methods •Uses real data from a number of areas •Examples utilize R code
Book Synopsis Longitudinal Data Analysis by : Jason Newsom
Download or read book Longitudinal Data Analysis written by Jason Newsom and published by Routledge. This book was released on 2013-06-19 with total page 407 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides accessible treatment to state-of-the-art approaches to analyzing longitudinal studies. Comprehensive coverage of the most popular analysis tools allows readers to pick and choose the techniques that best fit their research. The analyses are illustrated with examples from major longitudinal data sets including practical information about their content and design. Illustrations from popular software packages offer tips on how to interpret the results. Each chapter features suggested readings for additional study and a list of articles that further illustrate how to implement the analysis and report the results. Syntax examples for several software packages for each of the chapter examples are provided at www.psypress.com/longitudinal-data-analysis. Although many of the examples address health or social science questions related to aging, readers from other disciplines will find the analyses relevant to their work. In addition to demonstrating statistical analysis of longitudinal data, the book shows how to interpret and analyze the results within the context of the research design. The methods covered in this book are applicable to a range of applied problems including short- to long-term longitudinal studies using a range of sample sizes. The book provides non-technical, practical introductions to the concepts and issues relevant to longitudinal analysis. Topics include use of publicly available data sets, weighting and adjusting for complex sampling designs with longitudinal studies, missing data and attrition, measurement issues related to longitudinal research, the use of ANOVA and regression for average change over time, mediation analysis, growth curve models, basic and advanced structural equation models, and survival analysis. An ideal supplement for graduate level courses on data analysis and/or longitudinal modeling taught in psychology, gerontology, public health, human development, family studies, medicine, sociology, social work, and other behavioral, social, and health sciences, this multidisciplinary book will also appeal to researchers in these fields.
Book Synopsis Analysis of Longitudinal Data by : Peter Diggle
Download or read book Analysis of Longitudinal Data written by Peter Diggle and published by Oxford University Press, USA. This book was released on 2013-03-14 with total page 397 pages. Available in PDF, EPUB and Kindle. Book excerpt: This second edition has been completely revised and expanded to become the most up-to-date and thorough professional reference text in this fast-moving area of biostatistics. It contains an additional two chapters on fully parametric models for discrete repeated measures data and statistical models for time-dependent predictors.
Book Synopsis Structural Nonparametric Models for the Analysis of Longitudinal Data by : Colin O. Wu
Download or read book Structural Nonparametric Models for the Analysis of Longitudinal Data written by Colin O. Wu and published by Chapman and Hall/CRC. This book was released on 2016-01-15 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers the recent advancement of statistical methods for the analysis of longitudinal data. Real datasets from four large NIH-supported longitudinal clinical trials and epidemiological studies illustrate the practical applications of the statistical methods. This book focuses on the nonparametric approaches, which have gained tremendous popularity in biomedical studies. These approaches have the flexibility to answer many scientific questions that cannot be properly addressed by the existing parametric approaches, such as the linear and nonlinear mixed effects models.
Book Synopsis Applied Longitudinal Data Analysis by : Judith D. Singer
Download or read book Applied Longitudinal Data Analysis written by Judith D. Singer and published by Oxford University Press. This book was released on 2003-03-27 with total page 665 pages. Available in PDF, EPUB and Kindle. Book excerpt: Change is constant in everyday life. Infants crawl and then walk, children learn to read and write, teenagers mature in myriad ways, the elderly become frail and forgetful. Beyond these natural processes and events, external forces and interventions instigate and disrupt change: test scores may rise after a coaching course, drug abusers may remain abstinent after residential treatment. By charting changes over time and investigating whether and when events occur, researchers reveal the temporal rhythms of our lives. Applied Longitudinal Data Analysis is a much-needed professional book for empirical researchers and graduate students in the behavioral, social, and biomedical sciences. It offers the first accessible in-depth presentation of two of today's most popular statistical methods: multilevel models for individual change and hazard/survival models for event occurrence (in both discrete- and continuous-time). Using clear, concise prose and real data sets from published studies, the authors take you step by step through complete analyses, from simple exploratory displays that reveal underlying patterns through sophisticated specifications of complex statistical models. Applied Longitudinal Data Analysis offers readers a private consultation session with internationally recognized experts and represents a unique contribution to the literature on quantitative empirical methods. Visit http://www.ats.ucla.edu/stat/examples/alda.htm for: BL Downloadable data sets BL Library of computer programs in SAS, SPSS, Stata, HLM, MLwiN, and more BL Additional material for data analysis
Book Synopsis Measurement Error in Longitudinal Data by : Alexandru Cernat
Download or read book Measurement Error in Longitudinal Data written by Alexandru Cernat and published by Oxford University Press. This book was released on 2021-03-18 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: Longitudinal data is essential for understanding how the world around us changes. Most theories in the social sciences and elsewhere have a focus on change, be it of individuals, of countries, of organizations, or of systems, and this is reflected in the myriad of longitudinal data that are being collected using large panel surveys. This type of data collection has been made easier in the age of Big Data and with the rise of social media. Yet our measurements of the world are often imperfect, and longitudinal data is vulnerable to measurement errors which can lead to flawed and misleading conclusions. Measurement Error in Longitudinal Data tackles the important issue of how to investigate change in the context of imperfect data. It compiles the latest advances in estimating change in the presence of measurement error from several fields and covers the entire process, from the best ways of collecting longitudinal data, to statistical models to estimate change under uncertainty, to examples of researchers applying these methods in the real world. This book introduces the essential issues of longitudinal data collection, such as memory effects, panel conditioning (or mere measurement effects), the use of administrative data, and the collection of multi-mode longitudinal data. It also presents some of the most important models used in this area, including quasi-simplex models, latent growth models, latent Markov chains, and equivalence/DIF testing. Finally, the use of vignettes in the context of longitudinal data and estimation methods for multilevel models of change in the presence of measurement error are also discussed.
Book Synopsis Longitudinal Data Analysis by : Professor Catrien C J H C J H Bijleveld
Download or read book Longitudinal Data Analysis written by Professor Catrien C J H C J H Bijleveld and published by SAGE. This book was released on 1998-10-26 with total page 462 pages. Available in PDF, EPUB and Kindle. Book excerpt: By looking at the processes of change over time - by carrying out longitudinal studies - researchers answer questions about learning, development, educational growth, social change and medical outcomes. However, longitudinal research has many faces. This book examines all the main approaches as well as newer developments (such as structural equation modelling, multilevel modelling and optimal scaling) to enable the reader to gain a thorough understanding of the approach and make appropriate decisions about which technique can be applied to the research problem. Conceptual explanations are used to keep technical terms to a minimum; examples are provided for each approach; issues of design, measurement and significance are considered; and a standard notation is used throughout.
Book Synopsis Nonparametric Regression Analysis of Longitudinal Data by : Hans-Georg Müller
Download or read book Nonparametric Regression Analysis of Longitudinal Data written by Hans-Georg Müller and published by Springer. This book was released on 1988-01-01 with total page 199 pages. Available in PDF, EPUB and Kindle. Book excerpt:
