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False Rejection (false + rejection)

Distribution by Scientific Domains

Life Sciences	50%
Business, Economics, Finance and Accounting	50%

Selected Abstracts

The Impact of False Rejection Risk on Posterior Audit Risk Measurement

INTERNATIONAL JOURNAL OF AUDITING, Issue 1 2001
Anne D. Woodhead
This paper investigates false rejection risk, analysing the a priori relationship between the risk of false rejection and the more common risk of false acceptance, of an account balance by a substantive test. The paper uses probability theory to specify the relationship between these two risks and thus generate a model of posterior audit risk. The paper proceeds to investigate the relationship using the power function of basic statistics. This specifies the relationship between (i) the probability of rejecting the account balance and (ii) the size of the error which the balance contains. We argue that unless there is a discontinuity in the power function around the specified value of material error, then posterior audit risk will be unaffected by the substantive tests undertaken. Posterior risk will then be determined entirely by the assessed inherent and control risks. This conclusion is counter-intuitive to the approach to audit risk adopted by many professional pronouncements and results from the adoption of a mathematically rigorous definition of the risks encountered by the auditor. The primary conclusion is that the discontinuity arises under conditions of careful audit planning. If planning is careful, then false rejection risk contributes very little to posterior risk. In addition, there is very little difference between planned risk and posterior risk. [source]

Selection of evolutionary models for phylogenetic hypothesis testing using parametric methods

JOURNAL OF EVOLUTIONARY BIOLOGY, Issue 4 2001
B. C. Emerson
Recent molecular studies have incorporated the parametric bootstrap method to test a priori hypotheses when the results of molecular based phylogenies are in conflict with these hypotheses. The parametric bootstrap requires the specification of a particular substitutional model, the parameters of which will be used to generate simulated, replicate DNA sequence data sets. It has been both suggested that, (a) the method appears robust to changes in the model of evolution, and alternatively that, (b) as realistic model of DNA substitution as possible should be used to avoid false rejection of a null hypothesis. Here we empirically evaluate the effect of suboptimal substitution models when testing hypotheses of monophyly with the parametric bootstrap using data sets of mtDNA cytochrome oxidase I and II (COI and COII) sequences for Macaronesian Calathus beetles, and mitochondrial 16S rDNA and nuclear ITS2 sequences for European Timarcha beetles. Whether a particular hypothesis of monophyly is rejected or accepted appears to be highly dependent on whether the nucleotide substitution model being used is optimal. It appears that a parameter rich model is either equally or less likely to reject a hypothesis of monophyly where the optimal model is unknown. A comparison of the performance of the Kishino,Hasegawa (KH) test shows it is not as severely affected by the use of suboptimal models, and overall it appears to be a less conservative method with a higher rate of failure to reject null hypotheses. [source]

Analysis of multilocus models of association

GENETIC EPIDEMIOLOGY, Issue 1 2003
B. Devlin
Abstract It is increasingly recognized that multiple genetic variants, within the same or different genes, combine to affect liability for many common diseases. Indeed, the variants may interact among themselves and with environmental factors. Thus realistic genetic/statistical models can include an extremely large number of parameters, and it is by no means obvious how to find the variants contributing to liability. For models of multiple candidate genes and their interactions, we prove that statistical inference can be based on controlling the false discovery rate (FDR), which is defined as the expected number of false rejections divided by the number of rejections. Controlling the FDR automatically controls the overall error rate in the special case that all the null hypotheses are true. So do more standard methods such as Bonferroni correction. However, when some null hypotheses are false, the goals of Bonferroni and FDR differ, and FDR will have better power. Model selection procedures, such as forward stepwise regression, are often used to choose important predictors for complex models. By analysis of simulations of such models, we compare a computationally efficient form of forward stepwise regression against the FDR methods. We show that model selection includes numerous genetic variants having no impact on the trait, whereas FDR maintains a false-positive rate very close to the nominal rate. With good control over false positives and better power than Bonferroni, the FDR-based methods we introduce present a viable means of evaluating complex, multivariate genetic models. Naturally, as for any method seeking to explore complex genetic models, the power of the methods is limited by sample size and model complexity. Genet Epidemiol 25:36,47, 2003. © 2003 Wiley-Liss, Inc. [source]

Variable selection and oversampling in the use of smooth support vector machines for predicting the default risk of companies

JOURNAL OF FORECASTING, Issue 6 2009
Wolfgang Härdle
Abstract In the era of Basel II a powerful tool for bankruptcy prognosis is vital for banks. The tool must be precise but also easily adaptable to the bank's objectives regarding the relation of false acceptances (Type I error) and false rejections (Type II error). We explore the suitability of smooth support vector machines (SSVM), and investigate how important factors such as the selection of appropriate accounting ratios (predictors), length of training period and structure of the training sample influence the precision of prediction. Moreover, we show that oversampling can be employed to control the trade-off between error types, and we compare SSVM with both logistic and discriminant analysis. Finally, we illustrate graphically how different models can be used jointly to support the decision-making process of loan officers. Copyright © 2008 John Wiley & Sons, Ltd. [source]