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Estimation Bias (estimation + bias)

Distribution by Scientific Domains

Mathematics and Statistics	40%

Selected Abstracts

Efficiency of quantitative trait loci-assisted selection in correlations between identified and residual genotypes

ANIMAL SCIENCE JOURNAL, Issue 1 2008
Ching Y. LIN
ABSTRACT This study quantified the efficiency of quantitative traits loci (QTL)-assisted selection in the presence of correlations (,qr) between identified (q) and residual (r) genotypes. Two levels of heritability (h2 = 0.1 or 0.3), two levels of correlation (,qr = ,0.3 or 0.3) and five proportions of genetic variance explained by QTL detected (= 0.1, 0.2, 0.4, 0.6 or 0.8) were combined to give 20 scenarios in all. QTL-assisted selection placed a larger index weight on the QTL genotype than on the phenotype in 17 of 20 scenarios, yielding a greater response in the QTL genotype than in residual genotype. Although QTL-assisted selection was superior to phenotypic selection in all 20 scenarios, QTL-assisted selection showed a greater advantage over phenotypic selection when ,qr was positive than when ,qr was negative. Doubling the proportion of detected QTL variance to genetic variance does not result in a twofold increase in the genetic response to QTL-assisted selection, suggesting that economic returns diminish for each additional cost of detecting extra QTL. The correlation between q and r would make the interpretation (or prediction) of QTL effects difficult and QTL-assisted selection strategy must consider the joint effect of q and r. When q and r are not independent, a failure to account for ,qr in QTL-assisted selection would underestimate the genetic responses when ,qr is positive, but overestimate the genetic responses when ,qr is negative. Estimation bias is more serious at high heritability than at low heritability. Accounting for ,qr would improve the efficiency of QTL-assisted selection and the accuracy of QTL detection. The generalized procedure developed in this study allows for quantifying the efficiency of QTL-assisted selection and assessing estimation bias for ignoring the correlation between q and r for all possible combinations of h2, ,qr, and . [source]

Revisiting the corroboration effects of earnings and dividend announcements

ACCOUNTING & FINANCE, Issue 2 2006
Louis T. W. Cheng
D82; G14; M41 Abstract Using a unique market setting in Hong Kong, where (i) all firms release earnings and dividend information in the same announcement; (ii) corporate transparency is low; (iii) dividend income is non-taxable and (iv) corporate ownership is highly concentrated, we re-examine the corroboration effects of earnings and dividends. We use the control firm approach to avoid the return estimation bias resulting from observation clustering. We also add in variables and use econometric procedure to control for the potential impacts of earnings management, special dividends and heteroskedasticity. Our findings show that there exists a corroboration effect between the jointly announced signals. [source]

Efficiency of quantitative trait loci-assisted selection in correlations between identified and residual genotypes

High-Dimensional Cox Models: The Choice of Penalty as Part of the Model Building Process

BIOMETRICAL JOURNAL, Issue 1 2010
Axel Benner
Abstract The Cox proportional hazards regression model is the most popular approach to model covariate information for survival times. In this context, the development of high-dimensional models where the number of covariates is much larger than the number of observations ( ) is an ongoing challenge. A practicable approach is to use ridge penalized Cox regression in such situations. Beside focussing on finding the best prediction rule, one is often interested in determining a subset of covariates that are the most important ones for prognosis. This could be a gene set in the biostatistical analysis of microarray data. Covariate selection can then, for example, be done by L1 -penalized Cox regression using the lasso (Tibshirani (1997). Statistics in Medicine16, 385,395). Several approaches beyond the lasso, that incorporate covariate selection, have been developed in recent years. This includes modifications of the lasso as well as nonconvex variants such as smoothly clipped absolute deviation (SCAD) (Fan and Li (2001). Journal of the American Statistical Association96, 1348,1360; Fan and Li (2002). The Annals of Statistics30, 74,99). The purpose of this article is to implement them practically into the model building process when analyzing high-dimensional data with the Cox proportional hazards model. To evaluate penalized regression models beyond the lasso, we included SCAD variants and the adaptive lasso (Zou (2006). Journal of the American Statistical Association101, 1418,1429). We compare them with "standard" applications such as ridge regression, the lasso, and the elastic net. Predictive accuracy, features of variable selection, and estimation bias will be studied to assess the practical use of these methods. We observed that the performance of SCAD and adaptive lasso is highly dependent on nontrivial preselection procedures. A practical solution to this problem does not yet exist. Since there is high risk of missing relevant covariates when using SCAD or adaptive lasso applied after an inappropriate initial selection step, we recommend to stay with lasso or the elastic net in actual data applications. But with respect to the promising results for truly sparse models, we see some advantage of SCAD and adaptive lasso, if better preselection procedures would be available. This requires further methodological research. [source]

On Comparison of Mixture Models for Closed Population Capture,Recapture Studies

BIOMETRICS, Issue 2 2009
Chang Xuan Mao
Summary A mixture model is a natural choice to deal with individual heterogeneity in capture,recapture studies. Pledger (2000, Biometrics56, 434,442; 2005, Biometrics61, 868,876) advertised the use of the two-point mixture model. Dorazio and Royle (2003, Biometrics59, 351,364; 2005, Biometrics61, 874,876) suggested that the beta-binomial model has advantages. The controversy is related to the nonidentifiability of the population size (Link, 2003, Biometrics59, 1123,1130) and certain boundary problems. The total bias is decomposed into an intrinsic bias, an approximation bias, and an estimation bias. We propose to assess the approximation bias, the estimation bias, and the variance, with the intrinsic bias excluded when comparing different estimators. The boundary problems in both models and their impacts are investigated. Real epidemiological and ecological examples are analyzed. [source]