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Estimating Procedure (estimating + procedure)

Distribution by Scientific Domains
Mathematics and Statistics75%

Selected Abstracts

Estimation of population size for additive,multiplicative models based on continuous-time recapture experiments

ENVIRONMETRICS, Issue 8 2002
Yan Wang
Abstract An additive,multiplicative model and a Horvitz,Thompson-type estimator are proposed to estimate the unknown population size in a continuous-time recapture experiment. The proposed inference about the model parameters of the capture intensity is similar to that of Lin and Ying (1995). However, the population size in a recapture experiment is not known and a modification is needed. Simulation results are given to assess the properties of the proposed estimators and the associated inference procedures. A set of recapture data for deer mice is presented to demonstrate the performance of the estimating procedure. Copyright © 2002 John Wiley & Sons, Ltd. [source]

Accurate and time efficient estimation of the probability of error in bursty channels,

INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS, Issue 7 2003
M. Stevan Berber
Abstract A method and a technique for the probability of error estimation in digital channels with memory are developed and demonstrated. The expressions for the mean and variance of a random variable, representing a block of bits transmission in a bursty channel (channel with memory), are derived. The influence of the memory is expressed by a parameter called the memory factor. It is shown that the traditional Monte Carlo method can be applied for the probability of error estimation. In order to control the accuracy and increase the time efficiency of estimation this method is modified and a new method, called the modified Monte Carlo method, is proposed. Based on this modified method a technique of estimation with controlled accuracy is developed and demonstrated using data obtained by simulation. According to this technique the sample size is adjusted in the course of estimating procedure to give an accurate estimate of the probability of error for a minimum required time of estimation. Copyright © 2003 John Wiley & Sons, Ltd. [source]

Case,Cohort Analysis with Accelerated Failure Time Model

BIOMETRICS, Issue 1 2009
Lan Kong
Summary In a case,cohort design, covariates are assembled only for a subcohort that is randomly selected from the entire cohort and any additional cases outside the subcohort. This design is appealing for large cohort studies of rare disease, especially when the exposures of interest are expensive to ascertain for all the subjects. We propose statistical methods for analyzing the case,cohort data with a semiparametric accelerated failure time model that interprets the covariates effects as to accelerate or decelerate the time to failure. Asymptotic properties of the proposed estimators are developed. The finite sample properties of case,cohort estimator and its relative efficiency to full cohort estimator are assessed via simulation studies. A real example from a study of cardiovascular disease is provided to illustrate the estimating procedure. [source]

Application of the Time-Dependent ROC Curves for Prognostic Accuracy with Multiple Biomarkers

BIOMETRICS, Issue 1 2006
Yingye Zheng
Summary The rapid advancement in molecule technology has led to the discovery of many markers that have potential applications in disease diagnosis and prognosis. In a prospective cohort study, information on a panel of biomarkers as well as the disease status for a patient are routinely collected over time. Such information is useful to predict patients' prognosis and select patients for targeted therapy. In this article, we develop procedures for constructing a composite test with optimal discrimination power when there are multiple markers available to assist in prediction and characterize the accuracy of the resulting test by extending the time-dependent receiver operating characteristic (ROC) curve methodology (Heagerty, Lumley, and Pepe, 2000, Biometrics56, 337,344). We employ a modified logistic regression model to derive optimal linear composite scores such that their corresponding ROC curves are maximized at every false positive rate. We provide theoretical justification for using such a model for prognostic accuracy. The proposed method allows for time-varying marker effects and accommodates censored failure time outcome. When the effects of markers are approximately constant over time, we propose a more efficient estimating procedure under such models. We conduct numerical studies to evaluate the performance of the proposed procedures. Our results indicate the proposed methods are both flexible and efficient. We contrast these methods with an application concerning the prognostic accuracies of expression levels of six genes. [source]