Bias and Mean Square Error of Reliability Estimators under the One and Two Random Effects Models: The Effect of Non-Normality
Bias and Mean Square Error of Reliability Estimators under the One and Two Random Effects Models: The Effect of Non-Normality作者机构：Department of Cell Biology King Faisal Specialist Hospital and Research Center Riyadh Saudi Arabia Al-Faisal University College of Medicine Riyadh Saudi Arabia The Oncology Center King Faisal Specialist Hospital and Research Center Riyadh Saudi Arabia Department of Biostatistics Epidemiology and Scientific Computing King Faisal Specialist Hospital and Research Center Riyadh Saudi Arabia
出 版 物：《Open Journal of Statistics》 (统计学期刊（英文）)
年 卷 期：2016年第6卷第2期
页 面：254-273页
主 题：Rater’s Reliability Random Effects Models Multivariate Taylor’s Expansion Wald’s Confidence Interval Bootstrap Methods
摘 要：The coefficient of reliability is often estimated from a sample that includes few subjects. It is therefore expected that the precision of this estimate would be low. Measures of precision such as bias and variance depend heavily on the assumption of normality, which may not be tenable in practice. Expressions for the bias and variance of the reliability coefficient in the one and two way random effects models using the multivariate Taylor’s expansion have been obtained under the assumption of normality of the score (Atenafu et al. [1]). In the present paper we derive analytic expressions for the bias and variance, hence the mean square error when the measured responses are not normal under the one-way data layout. Similar expressions are derived in the case of the two-way data layout. We assess the effect of departure from normality on the sample size requirements and on the power of Wald’s test on specified hypotheses. We analyze two data sets, and draw comparisons with results obtained via the Bootstrap methods. It was found that the estimated bias and variance based on the bootstrap method are quite close to those obtained by the first order approximation using the Taylor’s expansion. This is an indication that for the given data sets the approximations are quite adequate.
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