## The log-exponentiated Weibull regression model for interval-censored data

## Testing hypotheses in the Birnbaum-Saunders distribution under type-II censored samples

## A Bayesian MCMC approach to survival analysis with doubly-censored data

## Analysis of Two-sample Censored Data Using a Semiparametric Mixture Model

## Imputation methods for doubly censored HIV data

## Weighted Moments Estimators of the Parameters for the Extreme Value Distribution Based on the Multiply Type II Censored Sample

## Sample size requirements for training high-dimensional risk predictors

## Sample size determination for paired right-censored data based on the difference of Kaplan-Meier estimates

## Median Tests for Censored Survival Data; a Contingency Table Approach

## Sample Size Planning For Detecting Treatment Effect On Biomarker Subset

## A simulation study of the error induced in one-sided reliability confidence bounds for the Weiball distribution using a small sample size with heavily censored data

## Exact goodness-of-fit tests for censored dats

## Estimating Correlation with Multiply Censored Data Arising from the Adjustment of Singly Censored Data

## Empirical Cummulative Density Function from a Univariate Censored Sample

## Data Transformations and Goodness-of-Fit Tests for Type-II Right Censored Samples

## Bayesian prediction of minimal repair times of a series system based on hybrid censored sample of components' lifetimes under Rayleigh distribution

## Weighted empirical likelihood in some two-sample semiparametric models with various types of censored data

## The Anderson-Darling test of fit for the power law distribution from left censored samples

## Estimating of $P(Y
Fonte: Universidade Cornell
Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 07/01/2008
Relevância na Pesquisa 36.15%
In this article, the estimation of reliability of a system is discussed
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## Managerial Decision Making in Censored Environments: Biased Judgment of Demand, Risk, and Employee Capability

Individuals have the tendency to believe that they have complete information when making decisions. In many contexts this propensity allows for swift, efficient, and generally effective decision making. However, individuals cannot always see a representative picture of the world in which they operate. This paper examines judgment in censored environments where a constraint, the censorship point, systematically distorts the sample observed by a decision maker. Random instances beyond the censorship point are observed at the censorship point, while instances below the censorship point are observed at their true value. Many important managerial decisions occur in censored environments, such as inventory, risk-taking, and employee evaluation decisions. This empirical work demonstrates a censorship bias - individuals tend to rely too heavily on the observed censored sample, biasing their beliefs about the underlying population. Further, the censorship bias is exacerbated for higher rates of censorship, higher variance in the population, and higher variability in the censorship points. Evidence from four studies demonstrates how the censorship bias can cause managers to underestimate demand for their goods, over-estimate risk in their environments...