Nonparametric Function Estimation With Left Truncated And Right Censored Data
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Nonparametric Hazard Rate Estimation with Left Truncated and Right Censored Data
| Author | : Jufen Chu |
| Publisher | : |
| Total Pages | : 186 |
| Release | : 2016 |
| Genre | : Censored observations (Statistics) |
| ISBN | : |
Nonparametric estimation of the hazard rate function, based on data modified by left truncation and/or right censoring, is considered. The hazard rate is not integrable over its support and hence it is traditionally estimated over a fixed interval under the mean integrated squared error (MISE) criterion. It is well known in the literature that neither left truncation nor right censoring affect the rate of the MISE convergence, but so far no results on how the modified data and the interval of estimation affect the MISE convergence have been known. To understand the affect, asymptotic theory of sharp minimax estimation is developed which indicates how the modified data and the interval of estimation affect the MISE convergence. The theory is complemented by presenting a data-driven estimator for small samples which is tested on numerical simulations and real data.
Nonparametric Tests for Censored Data
| Author | : Vilijandas Bagdonavicius |
| Publisher | : John Wiley & Sons |
| Total Pages | : 162 |
| Release | : 2013-02-07 |
| Genre | : Mathematics |
| ISBN | : 1118602137 |
This book concerns testing hypotheses in non-parametric models. Generalizations of many non-parametric tests to the case of censored and truncated data are considered. Most of the test results are proved and real applications are illustrated using examples. Theories and exercises are provided. The incorrect use of many tests applying most statistical software is highlighted and discussed.
Nonparametric Curve Estimation
| Author | : Sam Efromovich |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 423 |
| Release | : 2008-01-19 |
| Genre | : Mathematics |
| ISBN | : 0387226389 |
This book gives a systematic, comprehensive, and unified account of modern nonparametric statistics of density estimation, nonparametric regression, filtering signals, and time series analysis. The companion software package, available over the Internet, brings all of the discussed topics into the realm of interactive research. Virtually every claim and development mentioned in the book is illustrated with graphs which are available for the reader to reproduce and modify, making the material fully transparent and allowing for complete interactivity.
Analysis for Time-to-Event Data under Censoring and Truncation
| Author | : Hongsheng Dai |
| Publisher | : Academic Press |
| Total Pages | : 104 |
| Release | : 2016-10-06 |
| Genre | : Mathematics |
| ISBN | : 0081010087 |
Survival Analysis for Bivariate Truncated Data provides readers with a comprehensive review on the existing works on survival analysis for truncated data, mainly focusing on the estimation of univariate and bivariate survival function. The most distinguishing feature of survival data is known as censoring, which occurs when the survival time can only be exactly observed within certain time intervals. A second feature is truncation, which is often deliberate and usually due to selection bias in the study design. Truncation presents itself in different ways. For example, left truncation, which is often due to a so-called late entry bias, occurs when individuals enter a study at a certain age and are followed from this delayed entry time. Right truncation arises when only individuals who experienced the event of interest before a certain time point can be observed. Analyzing truncated survival data without considering the potential selection bias may lead to seriously biased estimates of the time to event of interest and the impact of risk factors. Assists statisticians, epidemiologists, medical researchers, and actuaries who need to understand the mechanism of selection bias Reviews existing works on survival analysis for truncated data, mainly focusing on the estimation of univariate and bivariate survival function Offers a guideline for analyzing truncated survival data
The Statistical Analysis of Interval-censored Failure Time Data
| Author | : Jianguo Sun |
| Publisher | : Springer |
| Total Pages | : 310 |
| Release | : 2007-05-26 |
| Genre | : Mathematics |
| ISBN | : 0387371192 |
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.
Statistical Models in Epidemiology, the Environment, and Clinical Trials
| Author | : M.Elizabeth Halloran |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 300 |
| Release | : 1999-10-29 |
| Genre | : Medical |
| ISBN | : 9780387989242 |
This IMA Volume in Mathematics and its Applications STATISTICAL MODELS IN EPIDEMIOLOGY, THE ENVIRONMENT,AND CLINICAL TRIALS is a combined proceedings on "Design and Analysis of Clinical Trials" and "Statistics and Epidemiology: Environment and Health. " This volume is the third series based on the proceedings of a very successful 1997 IMA Summer Program on "Statistics in the Health Sciences. " I would like to thank the organizers: M. Elizabeth Halloran of Emory University (Biostatistics) and Donald A. Berry of Duke University (Insti tute of Statistics and Decision Sciences and Cancer Center Biostatistics) for their excellent work as organizers of the meeting and for editing the proceedings. I am grateful to Seymour Geisser of University of Minnesota (Statistics), Patricia Grambsch, University of Minnesota (Biostatistics); Joel Greenhouse, Carnegie Mellon University (Statistics); Nicholas Lange, Harvard Medical School (Brain Imaging Center, McLean Hospital); Barry Margolin, University of North Carolina-Chapel Hill (Biostatistics); Sandy Weisberg, University of Minnesota (Statistics); Scott Zeger, Johns Hop kins University (Biostatistics); and Marvin Zelen, Harvard School of Public Health (Biostatistics) for organizing the six weeks summer program. I also take this opportunity to thank the National Science Foundation (NSF) and the Army Research Office (ARO), whose financial support made the workshop possible. Willard Miller, Jr.
Missing and Modified Data in Nonparametric Estimation
| Author | : Sam Efromovich |
| Publisher | : CRC Press |
| Total Pages | : 448 |
| Release | : 2018-03-12 |
| Genre | : Mathematics |
| ISBN | : 1351679848 |
This book presents a systematic and unified approach for modern nonparametric treatment of missing and modified data via examples of density and hazard rate estimation, nonparametric regression, filtering signals, and time series analysis. All basic types of missing at random and not at random, biasing, truncation, censoring, and measurement errors are discussed, and their treatment is explained. Ten chapters of the book cover basic cases of direct data, biased data, nondestructive and destructive missing, survival data modified by truncation and censoring, missing survival data, stationary and nonstationary time series and processes, and ill-posed modifications. The coverage is suitable for self-study or a one-semester course for graduate students with a prerequisite of a standard course in introductory probability. Exercises of various levels of difficulty will be helpful for the instructor and self-study. The book is primarily about practically important small samples. It explains when consistent estimation is possible, and why in some cases missing data should be ignored and why others must be considered. If missing or data modification makes consistent estimation impossible, then the author explains what type of action is needed to restore the lost information. The book contains more than a hundred figures with simulated data that explain virtually every setting, claim, and development. The companion R software package allows the reader to verify, reproduce and modify every simulation and used estimators. This makes the material fully transparent and allows one to study it interactively. Sam Efromovich is the Endowed Professor of Mathematical Sciences and the Head of the Actuarial Program at the University of Texas at Dallas. He is well known for his work on the theory and application of nonparametric curve estimation and is the author of Nonparametric Curve Estimation: Methods, Theory, and Applications. Professor Sam Efromovich is a Fellow of the Institute of Mathematical Statistics and the American Statistical Association.
The Statistical Analysis of Doubly Truncated Data
| Author | : Jacobo de Uña-Álvarez |
| Publisher | : John Wiley & Sons |
| Total Pages | : 196 |
| Release | : 2021-11-22 |
| Genre | : Medical |
| ISBN | : 1119951372 |
A thorough treatment of the statistical methods used to analyze doubly truncated data In The Statistical Analysis of Doubly Truncated Data, an expert team of statisticians delivers an up-to-date review of existing methods used to deal with randomly truncated data, with a focus on the challenging problem of random double truncation. The authors comprehensively introduce doubly truncated data before moving on to discussions of the latest developments in the field. The book offers readers examples with R code along with real data from astronomy, engineering, and the biomedical sciences to illustrate and highlight the methods described within. Linear regression models for doubly truncated responses are provided and the influence of the bandwidth in the performance of kernel-type estimators, as well as guidelines for the selection of the smoothing parameter, are explored. Fully nonparametric and semiparametric estimators are explored and illustrated with real data. R code for reproducing the data examples is also provided. The book also offers: A thorough introduction to the existing methods that deal with randomly truncated data Comprehensive explorations of linear regression models for doubly truncated responses Practical discussions of the influence of bandwidth in the performance of kernel-type estimators and guidelines for the selection of the smoothing parameter In-depth examinations of nonparametric and semiparametric estimators Perfect for statistical professionals with some background in mathematical statistics, biostatisticians, and mathematicians with an interest in survival analysis and epidemiology, The Statistical Analysis of Doubly Truncated Data is also an invaluable addition to the libraries of biomedical scientists and practitioners, as well as postgraduate students studying survival analysis.
Survival Analysis
| Author | : John P. Klein |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 508 |
| Release | : 2013-06-29 |
| Genre | : Medical |
| ISBN | : 1475727283 |
Making complex methods more accessible to applied researchers without an advanced mathematical background, the authors present the essence of new techniques available, as well as classical techniques, and apply them to data. Practical suggestions for implementing the various methods are set off in a series of practical notes at the end of each section, while technical details of the derivation of the techniques are sketched in the technical notes. This book will thus be useful for investigators who need to analyse censored or truncated life time data, and as a textbook for a graduate course in survival analysis, the only prerequisite being a standard course in statistical methodology.
