Nonparametric Quantile Regression with Censored Data PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Nonparametric Quantile Regression with Censored Data PDF full book. Access full book title Nonparametric Quantile Regression with Censored Data by D. M. Dabrowska. Download full books in PDF and EPUB format.
Author: D. M. Dabrowska
Publisher:
ISBN:
Category :
Languages : en
Pages : 42
Get Book Here
Book Description
Author: D. M. Dabrowska
Publisher:
ISBN:
Category :
Languages : en
Pages : 42
Get Book Here
Book Description
Author: Stanislav Volgushev
Publisher:
ISBN:
Category :
Languages : en
Pages : 120
Get Book Here
Book Description
Author: Stanislav Volgushev
Publisher:
ISBN:
Category :
Languages : en
Pages : 47
Get Book Here
Book Description
Author: Yuhlong Lio
Publisher:
ISBN:
Category : Nonparametric statistics
Languages : en
Pages : 162
Get Book Here
Book Description
Author: M.M. Desu
Publisher: CRC Press
ISBN: 1482285894
Category : Mathematics
Languages : en
Pages : 384
Get Book Here
Book Description
Balancing the "cookbook" approach of some texts with the more mathematical approach of others, Nonparametric Statistical Methods for Complete and Censored Data introduces commonly used non-parametric methods for complete data and extends those methods to right censored data analysis. Whenever possible, the authors derive their methodology from the
Author: Roger Koenker
Publisher: CRC Press
ISBN: 1498725295
Category : Mathematics
Languages : en
Pages : 463
Get Book Here
Book Description
Quantile regression constitutes an ensemble of statistical techniques intended to estimate and draw inferences about conditional quantile functions. Median regression, as introduced in the 18th century by Boscovich and Laplace, is a special case. In contrast to conventional mean regression that minimizes sums of squared residuals, median regression minimizes sums of absolute residuals; quantile regression simply replaces symmetric absolute loss by asymmetric linear loss. Since its introduction in the 1970's by Koenker and Bassett, quantile regression has been gradually extended to a wide variety of data analytic settings including time series, survival analysis, and longitudinal data. By focusing attention on local slices of the conditional distribution of response variables it is capable of providing a more complete, more nuanced view of heterogeneous covariate effects. Applications of quantile regression can now be found throughout the sciences, including astrophysics, chemistry, ecology, economics, finance, genomics, medicine, and meteorology. Software for quantile regression is now widely available in all the major statistical computing environments. The objective of this volume is to provide a comprehensive review of recent developments of quantile regression methodology illustrating its applicability in a wide range of scientific settings. The intended audience of the volume is researchers and graduate students across a diverse set of disciplines.
Author: Chul-Ki Kim
Publisher:
ISBN:
Category :
Languages : en
Pages : 174
Get Book Here
Book Description
Author: Chithran Vadaverkkot Vasudevan
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
In Survival analysis, it is vital to understand the effect of the covariates on the survival time. Commonly studied models are the Cox [1972] proportional hazards model and the accelerated failure time model. These methods mainly focus on one characteristic of the survival time. In reality, the association between the response and risk factors is not homogeneous always. This leads to the use of quantile regression [Koenker and Basset, 1978] models, which provide a global description of the association. In quantile regression modeling of the survival data, the problem of estimating the regression coefficients for extreme quantiles can be affected by severe censoring [Portnoy, 2003], especially when the sample size is small. In epidemiological studies, however, there are often times when only a subset of the whole study cohort is accurately observed. The rest of the cohort has only some auxiliary covariate available. The naive use of the auxiliary covariate in the model without the accurately measured covariate could lead to biased estimates. To deal with this problem in censored quantile regression, we propose a regression calibration based method when there is a linear relationship between the auxiliary covariate and the accurately measured covariate. When the relationship is non-linear, we propose a non-parametric kernel smoothing technique. We also propose an empirical likelihood [Owen, 1998, 2001] based weighted censored quantile regression to improve the efficiency of the censored quantile regression estimation by utilizing the auxiliary information about the target population parameters available through scientific facts/previous studies. The proposed estimators are consistent and have asymptotically Gaussian distributions. The efficiency gain compared to the existing methods is remarkable. These methods provide the possibilities of looking into extreme quantiles of the survival distribution. We also applied our proposed methods in real case examples.
Author: Abdelaati Daouia
Publisher: Springer Nature
ISBN: 3030732495
Category : Mathematics
Languages : en
Pages : 713
Get Book Here
Book Description
This book presents a unique collection of contributions on modern topics in statistics and econometrics, written by leading experts in the respective disciplines and their intersections. It addresses nonparametric statistics and econometrics, quantiles and expectiles, and advanced methods for complex data, including spatial and compositional data, as well as tools for empirical studies in economics and the social sciences. The book was written in honor of Christine Thomas-Agnan on the occasion of her 65th birthday. Given its scope, it will appeal to researchers and PhD students in statistics and econometrics alike who are interested in the latest developments in their field.
Author: Thomas R. Fleming
Publisher: John Wiley & Sons
ISBN: 111815066X
Category : Mathematics
Languages : en
Pages : 454
Get Book Here
Book Description
The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. "The book is a valuable completion of the literature in this field. It is written in an ambitious mathematical style and can be recommended to statisticians as well as biostatisticians." -Biometrische Zeitschrift "Not many books manage to combine convincingly topics from probability theory over mathematical statistics to applied statistics. This is one of them. The book has other strong points to recommend it: it is written with meticulous care, in a lucid style, general results being illustrated by examples from statistical theory and practice, and a bunch of exercises serve to further elucidate and elaborate on the text." -Mathematical Reviews "This book gives a thorough introduction to martingale and counting process methods in survival analysis thereby filling a gap in the literature." -Zentralblatt für Mathematik und ihre Grenzgebiete/Mathematics Abstracts "The authors have performed a valuable service to researchers in providing this material in [a] self-contained and accessible form. . . This text [is] essential reading for the probabilist or mathematical statistician working in the area of survival analysis." -Short Book Reviews, International Statistical Institute Counting Processes and Survival Analysis explores the martingale approach to the statistical analysis of counting processes, with an emphasis on the application of those methods to censored failure time data. This approach has proven remarkably successful in yielding results about statistical methods for many problems arising in censored data. A thorough treatment of the calculus of martingales as well as the most important applications of these methods to censored data is offered. Additionally, the book examines classical problems in asymptotic distribution theory for counting process methods and newer methods for graphical analysis and diagnostics of censored data. Exercises are included to provide practice in applying martingale methods and insight into the calculus itself.
