Download or read book Statistical Inference for Correlated Data Based on Censored Observations written by Jung Wook Park and published by . This book was released on 2005 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: Keywords: censored data, correlated data, imputation method, Gibbs sampling, truncated multivariate Gaussian.

Download or read book Statistical Inference for Correlated Data Based on Censored Observations written by and published by . This book was released on 2004 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Many physical quantities measured over time and space are often observed with data irregularities, such as truncation (detection limit) or censoring. Practitioners often disregard censored data cases which may result in inefficient estimates. On the other hand, censored data treated as observed values will lead to biased estimates. For instance, the data values collected by a monitoring device may have a specific detection limit and the device records the value with its limit, or a constant exceeding the limit value, when the real value exceeds the limit. We present an attractive remedy for handling censored or truncated data collected over time or space. Our method produces (asymptotically) unbiased estimates that are more efficient than the estimates based on treating censored observations as completely observed. In particular, we introduce an imputation method particularly well suited for fitting statistical models dealing with correlated observations in the presence of censored data. Our proposed imputation method involves generating random samples from the conditional distribution of the censored data given the (completely) observed data and current estimates of the parameters. The parameter estimates are then updated based on imputed and completely observed data until convergence. Under Gaussian processes, such a conditional distribution turns out to be a truncated multivariate normal distribution. We use a Gibbs sampling method to generate samples from such truncated multivariate normal distributions. We demonstrate the effectiveness of the technique for a problem common to many correlated data sets and describe its application to several other frequently encountered situations. First, we discuss the use of an imputation technique for a stationary time series data assuming an autoregressive moving average model. Then, we relax the model assumption and discuss how the imputation method works with a nonparametric estimation of a covariance matrix. The use of th.

Download or read book Inference for Time to Event and Sojourn Time Data Under Right Censoring Using Reweighting Approaches written by Jie Fan and published by . This book was released on 2010 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this dissertation research, we aim to solve problems of two types of survival data, clustered survival data with potentially informative cluster size and sojourn time data. The methods for these two types of data are different. However, both data have right censored observations, and we use reweighting approaches to deal with the censoring issue. In the first part of the dissertation research, we consider marginal AFT models for correlated survival data with potentially informative cluster size. Informative cluster size means that the size of the correlated groups may be predictive of their survival characteristics. Two competing proposals, cluster-weighted AFT (CWAFT) marginal model and non-cluster-weighted AFT (NCW AFT) marginal model, are investigated. Simulation and theoretical results show that the CW AFT approach produces unbiased parameter estimation, but that the NCWAFT model does not when the cluster size is informative. We use probability-probability plots to investigate statistical properties of confidence intervals and adopt Wald tests to examine power properties for the CW AFT model. To illustrate our analysis, we apply the CWAFT model to a dental study data set. In the second part of the dissertation research, we consider the problem of comparing sojourn time distributions of a transient state in a general multi state system in two samples (groups) when the transition times are right censored. Under this setup, the censoring induced on the weight times is complex since both the state entry and exit are subjected to right censoring. Using the reweighting principle, a two sample Mann-Whitney type U-statistic is constructed that compares only the uncensored state sojourn times from the two distributions. A second Mann-Whitney type statistic is also constructed using a different reweighting that allows for comparison when one of the two sojourn times is either uncensored or singly censored. While both statistics are asymptotically unbiased and reduce to the standard Mann-Whitney statistic when there is no censoring, the second statistic has smaller variance since it effectively uses larger pairs of samples. Asymptotic normality of these statistics are established. A test of comparing the equality of sojourn time distributions in two independent samples is constructed by symmetrizing the pair specific Mann-Whitney type statistics mentioned above. The testing methodology is illustrated using a kidney disease patients data set.

Download or read book The Statistical Analysis of Interval censored Failure Time Data written by Jianguo Sun and published by Springer. This book was released on 2007-05-26 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book collects and unifies statistical models and methods that have been proposed for analyzing interval-censored failure time data. It provides the first comprehensive coverage of the topic of interval-censored data and complements the books on right-censored data. The focus of the book is on nonparametric and semiparametric inferences, but it also describes parametric and imputation approaches. This book provides an up-to-date reference for people who are conducting research on the analysis of interval-censored failure time data as well as for those who need to analyze interval-censored data to answer substantive questions.

Download or read book Statistical Inference for Censored Multivariate Distributions Based on Induced Order Statistics written by F. E. Harrell and published by . This book was released on 1979 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book New Developments in Statistical Modeling Inference and Application written by Zhezhen Jin and published by Springer. This book was released on 2016-10-28 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: The papers in this volume represent the most timely and advanced contributions to the 2014 Joint Applied Statistics Symposium of the International Chinese Statistical Association (ICSA) and the Korean International Statistical Society (KISS), held in Portland, Oregon. The contributions cover new developments in statistical modeling and clinical research: including model development, model checking, and innovative clinical trial design and analysis. Each paper was peer-reviewed by at least two referees and also by an editor. The conference was attended by over 400 participants from academia, industry, and government agencies around the world, including from North America, Asia, and Europe. It offered 3 keynote speeches, 7 short courses, 76 parallel scientific sessions, student paper sessions, and social events.

Download or read book Statistical Inference for Correlated Binary Data from Bilateral Studies written by Yanbo Pei and published by . This book was released on 2009 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Dissertation Abstracts International written by and published by . This book was released on 2007 with total page 854 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Principles of Statistical Inference written by D. R. Cox and published by Cambridge University Press. This book was released on 2006-08-10 with total page 227 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this definitive book, D. R. Cox gives a comprehensive and balanced appraisal of statistical inference. He develops the key concepts, describing and comparing the main ideas and controversies over foundational issues that have been keenly argued for more than two-hundred years. Continuing a sixty-year career of major contributions to statistical thought, no one is better placed to give this much-needed account of the field. An appendix gives a more personal assessment of the merits of different ideas. The content ranges from the traditional to the contemporary. While specific applications are not treated, the book is strongly motivated by applications across the sciences and associated technologies. The mathematics is kept as elementary as feasible, though previous knowledge of statistics is assumed. The book will be valued by every user or student of statistics who is serious about understanding the uncertainty inherent in conclusions from statistical analyses.

Download or read book Computer Age Statistical Inference Student Edition written by Bradley Efron and published by Cambridge University Press. This book was released on 2021-06-17 with total page 514 pages. Available in PDF, EPUB and Kindle. Book excerpt: The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and influence. 'Data science' and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? How does it all fit together? Now in paperback and fortified with exercises, this book delivers a concentrated course in modern statistical thinking. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov Chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. Each chapter ends with class-tested exercises, and the book concludes with speculation on the future direction of statistics and data science.

Download or read book Handbook of Statistical Methods for Randomized Controlled Trials written by KyungMann Kim and published by CRC Press. This book was released on 2021-08-23 with total page 655 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical concepts provide scientific framework in experimental studies, including randomized controlled trials. In order to design, monitor, analyze and draw conclusions scientifically from such clinical trials, clinical investigators and statisticians should have a firm grasp of the requisite statistical concepts. The Handbook of Statistical Methods for Randomized Controlled Trials presents these statistical concepts in a logical sequence from beginning to end and can be used as a textbook in a course or as a reference on statistical methods for randomized controlled trials. Part I provides a brief historical background on modern randomized controlled trials and introduces statistical concepts central to planning, monitoring and analysis of randomized controlled trials. Part II describes statistical methods for analysis of different types of outcomes and the associated statistical distributions used in testing the statistical hypotheses regarding the clinical questions. Part III describes some of the most used experimental designs for randomized controlled trials including the sample size estimation necessary in planning. Part IV describe statistical methods used in interim analysis for monitoring of efficacy and safety data. Part V describe important issues in statistical analyses such as multiple testing, subgroup analysis, competing risks and joint models for longitudinal markers and clinical outcomes. Part VI addresses selected miscellaneous topics in design and analysis including multiple assignment randomization trials, analysis of safety outcomes, non-inferiority trials, incorporating historical data, and validation of surrogate outcomes.

Download or read book Statistical Inference for Gap Data written by Liqiang Yang and published by . This book was released on 2000 with total page 72 pages. Available in PDF, EPUB and Kindle. Book excerpt: Keywords: Censored data, Gap, Interval censoring, Missing data, Imputed.

Download or read book Statistical Inference in Models Based on the Percentile Residual Lifetime Function written by Jae Kee Song and published by . This book was released on 1988 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Statistical Inference for Multivariate Event History Data Under Incomplete Observation written by Rinku Sutradhar and published by . This book was released on 2005 with total page 382 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Tools for Statistical Inference written by Martin A. Tanner and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 215 pages. Available in PDF, EPUB and Kindle. Book excerpt: A unified introduction to a variety of computational algorithms for likelihood and Bayesian inference. This third edition expands the discussion of many of the techniques presented, and includes additional examples as well as exercise sets at the end of each chapter.

Download or read book Statistical Analysis of Panel Count Data written by Jianguo Sun and published by Springer Science & Business Media. This book was released on 2013-10-09 with total page 283 pages. Available in PDF, EPUB and Kindle. Book excerpt: Panel count data occur in studies that concern recurrent events, or event history studies, when study subjects are observed only at discrete time points. By recurrent events, we mean the event that can occur or happen multiple times or repeatedly. Examples of recurrent events include disease infections, hospitalizations in medical studies, warranty claims of automobiles or system break-downs in reliability studies. In fact, many other fields yield event history data too such as demographic studies, economic studies and social sciences. For the cases where the study subjects are observed continuously, the resulting data are usually referred to as recurrent event data. This book collects and unifies statistical models and methods that have been developed for analyzing panel count data. It provides the first comprehensive coverage of the topic. The main focus is on methodology, but for the benefit of the reader, the applications of the methods to real data are also discussed along with numerical calculations. There exists a great deal of literature on the analysis of recurrent event data. This book fills the void in the literature on the analysis of panel count data. This book provides an up-to-date reference for scientists who are conducting research on the analysis of panel count data. It will also be instructional for those who need to analyze panel count data to answer substantive research questions. In addition, it can be used as a text for a graduate course in statistics or biostatistics that assumes a basic knowledge of probability and statistics.

Download or read book Survival Analysis written by H J Vaman and published by CRC Press. This book was released on 2022-08-26 with total page 303 pages. Available in PDF, EPUB and Kindle. Book excerpt: Survival analysis generally deals with analysis of data arising from clinical trials. Censoring, truncation, and missing data create analytical challenges and the statistical methods and inference require novel and different approaches for analysis. Statistical properties, essentially asymptotic ones, of the estimators and tests are aptly handled in the counting process framework which is drawn from the larger arm of stochastic calculus. With explosion of data generation during the past two decades, survival data has also enlarged assuming a gigantic size. Most statistical methods developed before the millennium were based on a linear approach even in the face of complex nature of survival data. Nonparametric nonlinear methods are best envisaged in the Machine Learning school. This book attempts to cover all these aspects in a concise way. Survival Analysis offers an integrated blend of statistical methods and machine learning useful in analysis of survival data. The purpose of the offering is to give an exposure to the machine learning trends for lifetime data analysis. Features: Classical survival analysis techniques for estimating statistical functional and hypotheses testing Regression methods covering the popular Cox relative risk regression model, Aalen’s additive hazards model, etc. Information criteria to facilitate model selection including Akaike, Bayes, and Focused Penalized methods Survival trees and ensemble techniques of bagging, boosting, and random survival forests A brief exposure of neural networks for survival data R program illustration throughout the book