2 edition of **Shrinkage estimation in nonparametric Bayesian survival analysis** found in the catalog.

Shrinkage estimation in nonparametric Bayesian survival analysis

Kamta Rai

- 259 Want to read
- 22 Currently reading

Published
**1979**
by Rand Corporation in Santa Monica, Calif
.

Written in English

- Bayesian statistical decision theory.,
- Nonparametric statistics.,
- Distribution (Probability theory),
- Medical statistics.

**Edition Notes**

Bibliography: p. 32-33.

Statement | Kamta Rai, V. Susarla, and John Van Ryzin. |

Series | The Rand paper series ; P-6357, Wheeler, Roxann -- P-6357. |

Contributions | Susarla, V. 1943-., Van Ryzin, John. |

The Physical Object | |
---|---|

Pagination | 33 p. ; |

Number of Pages | 33 |

ID Numbers | |

Open Library | OL16465211M |

Running headline: Nonparametric Bayesian survival analysis 1. Introduction The combination of the Bayesian paradigm and nonparametric methodology requires the construction of priors on function spaces. The area of Bayesian nonparametrics has grown rapidly following the work of Ferguson () on the Dirichlet process (DP), a random. 1. Motivation for nonparametric Bayesian methods. There are many ways to use data, whether experimental or observational, to better understand the world and to make better decisions. The Bayesian approach distinguishes itself from other approaches with two distinct sources of sound foundational support. The first is the theory of subjective probability, Cited by: 6.

The following are some the books on survival analysis that I have found useful. There are of course many other good ones not listed. Modelling Survival Data in Medical Research, by Collett (2nd edition ) Survival and Event History Analysis: A Process Point of View, by Aalen, Borgan and Gjessing. This book provides a comprehensive treatment of Bayesian survival analysis. It presents a balance between theory and applications, and for each class of models discussed, detailed examples and analyses from case studies are presented whenever possible.

the presence of shrinkage The problem of shrinkage in showed examples associated to the diagnostics solely. Estimation is not affected. Consequences of shrinkage ignorance: wrong decisions - increased time for data analysis - wrong models Shrinkage phenomenon is likely to affect other type of model diagnostics such as: GAM - CWRES. This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. Rather than providing an encyclopedic review of probability models, the book's structure follows a data analysis perspective. As such, the chapters are organized by traditional data analysis problems. In selecting specific nonparametric models, simpler and .

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Shrinkage Estimation in Nonparametric Bayesian Survival Analysis Author: Kamta Rai Subject: Discusses shrinkage estimation in nonparametric Bayesian survival analysis using censored data. Created Date: 10/28/ PM. Discusses shrinkage estimation in nonparametric Bayesian survival analysis using censored data.

The shrinkage estimators proposed are based on estimating the parameter measure of a prior Dirichlet process in a nonparametric Bayesian survival curve estimator which is the posterior mean of this process.

The shrinkage is toward a prior family of exponential survival by: 2. Shrinkage Estimation in Nonparametric Bayesian Survival Analysis Kamta Rai,V. Susarla,John Van Ryzin — Bayesian statistical decision theory Author: Kamta Rai,V.

Susarla,John Van Ryzin. Sequential Estimation is the first, single-source guide to thetheory and practice of both classical and modern sequentialestimation techniques--including parametric and nonparametricmethods.

Researchers in sequential analysis will appreciate theunified, logically integrated treatment of the subject, as well ascoverage of important contemporary procedures not covered in moregeneral sequential analysis texts 4/5(1).

This paper discusses shrinkage estimation in nonparametric Bayesian survival analysis using censored data. The shrinkage estimators proposed are based on estimating the parameter measure of a prior.

This is the only book I have seen on the topic of Bayesian nonparametric statistics. The theory depends a great deal on the using of Dirichlet prior distributions and is somwhat new and advanced. this is an excellent and well-written text but it will not be suited for you umless you have a solid grounding in probability and statistics and have 5/5(2).

It is not surprising either that the Bayesian estimator performs better than the nonparametric Turnbull estimator, because the Bayesian estimator shrinks the nonparametric estimator towards the true survival curve.

This relative advantage of the Bayesian estimator increases considerably when the censoring intervals have a larger by: This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. Rather than providing an encyclopedic review of probability models, the book’s structure follows a data analysis perspective.

As such, the chapters are organized by traditional data analysis problems. I am submitting herewith a dissertation written by Artin Armagan entitled “Bayesian Shrinkage Estimation and Model Selection”. I have examined the ﬁnal paper copy of this dissertation for form and content and recommend that it be accepted in partial fulﬁllment of the requirements for the degree of Doctor of Philosophy, with a major.

Shrinkage is generally implicit in Bayesian estimation. For example, suppose you are trying to estimate a basketball player’s free throw shooting ability. You start with a Beta(a,b) prior, and observe X free throw attempts with Y made. Your estima.

While penalized splines with conditionally Gaussian smoothness priors form the basis for estimating nonparametric and flexible time-varying effects, regularization of high-dimensional covariate vectors is based on scale mixture of normals priors, including among others the Bayesian ridge and lasso as well as a spike and slab prior for shrinkage Cited by: 2.

Implementation of a full Bayesian non-parametric analysis involving neutral to the right processes (apart from the special case of the Dirichlet.

Parametric vs Nonparametric Models • Parametric models assume some ﬁnite set of heparameters, future predictions, x, are independent of the observed data, D: P(x|,D)=P(x|) therefore capture everything there is to know about the data.

• So the complexity of the model is bounded even if the amount of data is Size: KB. Additional Physical Format: Online version: Rai, Kamta. Shrinkage estimation in nonparametric Bayesian survival analysis. Santa Monica, Calif.: Rand Corporation, The degree of shrinkage for each subgroup’s estimate of treatment effect is illustrated in Figure 2.

When the odds ratios derived from the raw subgroup data are far fromor when they are based on less underlying data (i.e., when there is greater uncertainty), then the resulting shrinkage is by: Fundamentals of Nonparametric Bayesian Inference is the first book to comprehensively cover models, methods, and theories of Bayesian nonparametrics.

Readers can learn basic ideas and intuitions as well as rigorous treatments of underlying theories and computations from this wonderful book.'Cited by: A compilation of original articles by Bayesian experts, this volume presents perspectives on recent developments on nonparametric and semiparametric methods in Bayesian statistics.

The articles discuss how to conceptualize and develop Bayesian models using rich classes of nonparametric and. The simplest situation encountered in survival analysis is the nonparametric estimation of a survival distribution function based on a right-censored sample of observation times (X ˜ 1,X ˜ n).Here, each X ˜ i is either a survival time X i, in which case the failure/censoring indicator D i takes the value 1, or it is a right-censoring time, say U i, and then D i = 0.

2 Cause-speci c competing-risk survival analysis: The R Package CFC 1. Introduction Motivation: Consistent propagation and calculation of uncertainty using predictive poste-rior distributions is a key advantage of Bayesian frameworks (Gelman and Hill), par-ticularly in survival analysis, where predicted entities such as survival probability File Size: KB.

Bayesian nonparametrics has grown tremendously in the last three decades, especially in the last few years. This book is the first systematic treatment of Bayesian nonparametric methods and the theory behind them.

While the book is of special interest to Bayesians, it will also appeal to. Parametric and Bayesian Modeling of Reliability and Survival Analysis by Carlos A. Molinares A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Mathematics and Statistics College of Arts and Sciences University of South Florida Major Professor: Christos P.

Tsokos, Ph.D.This article presents the scope of nonparametric and semi-parametric Bayesian methods for the analysis of survival data using models based on either the hazard or the intensity function.

The nonparametric part of every model is assumed to have a suitable prior by: Doss H. Bayesian nonparametric estimation for incomplete data via successive substitution sampling. Annals of Statistics. ; – Doss H, Huffer F. Monte Carlo methods for Bayesian analysis of survival data using mixtures of Dirichlet priors.

Technical report; Department of Statistics, Ohio State University. Cited by: