A modified bootstrap for importance sampled data1
نویسندگان
چکیده
We are testing a modified bootstrap technique for importance sampled data from SimSET (a simulation system for emission tomography). The bootstrap allows us to simultaneously produce multiple raw data sets from a single simulation while at the same time reducing the weight variation caused by importance sampling. This combination may greatly reduce the CPU time required to produce the multiple image realizations needed for ROC studies. Initial testing indicates that the mean and variance of medium and high count bins in the raw data and high-activity regions of interest (ROIs) are reproduced relatively accurately.
منابع مشابه
Evaluation of Palm Groves Technical Efficiency Using Bootstrap Data Envelopment Analysis: A Case Study of Roodkhanehbar Area, Iran
Roodkhnehbar area, having approximately 111 thousands of Keriteh palm trees, is one of the most important areas of date production in the Rudan County[1]and the source of peoples’ income in this area, directly or indirectly. As a result, its production efficiency has a critical importance to the orchardists in this region. This study aims to evaluate technical efficiency of palm groves in this ...
متن کاملBootstrap and MLS methods-based individual bioequivalence assessment
It is a one-sided hypothesis testing process for assessing bioequivalence. Bootstrap and modified large-sample(MLS) methods are considered to study individual bioequivalence(IBE), type I error and power of hypothesis tests are simulated and compared with FDA(2001). The results show that modified large-sample method is equivalent to the method of FDA(2001) . Keywords—individual bioequivalence; b...
متن کاملBootstrapping continuous-time autoregressive processes
We develop a bootstrap procedure for Lévy-driven continuous-time autoregressive (CAR) processes observed at discrete regularly-spaced times. It is well known that a regularly sampled stationary Ornstein–Uhlenbeck process [i.e. a CAR(1) process] has a discrete-time autoregressive representation with i.i.d. noise. Based on this representation a simple bootstrap procedure can be found. Since regul...
متن کاملOn the Validity of the Pairs Bootstrap for Lasso Estimators
We study the validity of the pairs bootstrap for Lasso estimators in linear regression models with random covariates and heteroscedastic error terms. We show that the naive pairs bootstrap does not consistently estimate the distribution of the Lasso estimator. In particular, we identify two different sources for the failure of the bootstrap. First, in the bootstrap samples the Lasso estimator f...
متن کاملImportance Sampled Learning Ensembles
Learning a function of many arguments is viewed from the perspective of high– dimensional numerical quadrature. It is shown that many of the popular ensemble learning procedures can be cast in this framework. In particular randomized methods, including bagging and random forests, are seen to correspond to random Monte Carlo integration methods each based on particular importance sampling strate...
متن کامل