Topic
Resampling
About: Resampling is a research topic. Over the lifetime, 5428 publications have been published within this topic receiving 242291 citations.
Papers published on a yearly basis
Papers
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Book•
30 Jan 2013
TL;DR: This book is comprehensive for learning S–PLUS—only a single chapter deals strictly with statistic analysis, though perhaps R novices would be better served by the recent book by Crawley (2005), for which the review by Ng (2006) appears elsewhere in this issue.
48 citations
01 Jan 2004
TL;DR: In this paper, a comparison is made between four frequently encountered resampling algorithms for particle filters, and a theoretical framework is introduced to understand and explain the complexity of these algorithms.
Abstract: In this report a comparison is made between four frequently encountered resampling algorithms for particle filters. A theoretical framework is introduced to be able to understand and explain the di ...
48 citations
Journal Article•
Abstract: Ying, Jung and Wei (1995) proposed an estimation procedure for the censored median regression model that regresses the median of the survival time, or its transform, on the covariates. The procedure requires solving complicated nonlinear equations and thus can be very difficult to implement in practice, especially when there are multiple covariates. Moreover, the asymptotic covariance matrix of the estimator involves the density of the errors that cannot be estimated reliably. In this paper, we propose a new estimator for the censored median regression model. Our estimation procedure involves solving some convex minimization problems and can be easily implemented through linear programming (Koenker and D’Orey (1987)). In addition, a resampling method is presented for estimating the covariance matrix of the new estimator. Numerical studies indicate the superiority of the finite sample performance of our estimator over that in Ying, Jung and Wei (1995).
48 citations
01 Jan 2008
TL;DR: In this article, a semiparametric additive hazards model is proposed for analysis of competing risk data with missing cause of failure, and different estimating equation approaches using the inverse probability weighted and double robust techniques are proposed for estimating the regression parameters of interest.
Abstract: Competing risks data arise when study subjects may experience several different types of failure. It is common that the cause of failure is missing due to various reasons. Analysis of competing risks data with missing cause of failure has received considerable attention recently (Goetghebeur and Ryan (1995), Lu and Tsiatis (2001), Gao and Tsiatis (2005), among others). In this article, we study the semiparametric additive hazards model for analysis of competing risk data with missing cause of failure. Different estimating equation approaches using the inverse probability weighted and double robust techniques are proposed for estimating the regression parameters of interest. The resulting estimators have closed forms and their theoretical properties are established for inference. Simultaneous confidence bands of survival curves are constructed using a resampling technique. Simulations and an example show that the proposed approach is appropriate for practical use.
48 citations
TL;DR: A new procedure is proposed that yields exact Monte Carlo tests for any positive value of B, the number of simulations, and is likely to be most useful when simulation is expensive.
Abstract: Conventional procedures for Monte Carlo and bootstrap tests require that B, the number of simulations, satisfy a specific relationship with the level of the test. Otherwise, a test that would instead be exact will either overreject or underreject for finite B. We present expressions for the rejection frequencies associated with existing procedures and propose a new procedure that yields exact Monte Carlo tests for any positive value of B. This procedure, which can also be used for bootstrap tests, is likely to be most useful when simulation is expensive.
48 citations