The expected performance of single nucleotide polymorphism loci in paternity testing

We discuss the utility of single nucleotide polymorphism loci for full trio and mother-unavailable paternity testing cases, in the presence of population substructure and relatedness of putative and actual fathers. We focus primarily on the expected number of loci required to gain specified probabilities of mismatches, and report the expected proportion of paternity indices greater than three threshold values for these loci. (c) 2004 Elsevier Ireland Ltd. All rights reserved.

Mapping quantitative trait loci for flag leave senescence as a yield determinant in winter wheat under optimal and drought stressed environments

The timing of flag leaf senescence (FLS) is an important determinant of yield under stress and optimal environments. A doubled haploid population derived from crossing the photo period-sensitive variety Beaver,with the photo period-insensitive variety Soissons, varied significantly for this trait, measured as the percent green flag leaf area remaining at 14 days and 35 days after anthesis. This trait also showed a significantly positive correlation with yield under variable environmental regimes. QTL analysis based on a genetic map derived from 48 doubled haploid lines using amplified fragment length polymorphism (AFLP) and simple sequence repeat (SSR) markers, revealed the genetic control of this trait. The coincidence of QTL for senescence on chromosomes 2B and 2D under drought-stressed and optimal environments, respectively, indicate a complex genetic mechanism of this trait involving the re-mobilisation of resources from the source to the sink during senescence.

Sparse kernel density construction using orthogonal forward regression with leave-one-out test score and local regularization

This paper presents an efficient construction algorithm for obtaining sparse kernel density estimates based on a regression approach that directly optimizes model generalization capability. Computational efficiency of the density construction is ensured using an orthogonal forward regression, and the algorithm incrementally minimizes the leave-one-out test score. A local regularization method is incorporated naturally into the density construction process to further enforce sparsity. An additional advantage of the proposed algorithm is that it is fully automatic and the user is not required to specify any criterion to terminate the density construction procedure. This is in contrast to an existing state-of-art kernel density estimation method using the support vector machine (SVM), where the user is required to specify some critical algorithm parameter. Several examples are included to demonstrate the ability of the proposed algorithm to effectively construct a very sparse kernel density estimate with comparable accuracy to that of the full sample optimized Parzen window density estimate. Our experimental results also demonstrate that the proposed algorithm compares favorably with the SVM method, in terms of both test accuracy and sparsity, for constructing kernel density estimates.

Fast kernel classifier construction using orthogonal forward selection to minimise leave-one-out misclassification rate

We propose a simple yet computationally efficient construction algorithm for two-class kernel classifiers. In order to optimise classifier's generalisation capability, an orthogonal forward selection procedure is used to select kernels one by one by minimising the leave-one-out (LOO) misclassification rate directly. It is shown that the computation of the LOO misclassification rate is very efficient owing to orthogonalisation. Examples are used to demonstrate that the proposed algorithm is a viable alternative to construct sparse two-class kernel classifiers in terms of performance and computational efficiency.

A fast linear-in-the-parameters classifier construction algorithm using orthogonal forward selection to minimize leave-one-out misclassification rate

We propose a simple and computationally efficient construction algorithm for two class linear-in-the-parameters classifiers. In order to optimize model generalization, a forward orthogonal selection (OFS) procedure is used for minimizing the leave-one-out (LOO) misclassification rate directly. An analytic formula and a set of forward recursive updating formula of the LOO misclassification rate are developed and applied in the proposed algorithm. Numerical examples are used to demonstrate that the proposed algorithm is an excellent alternative approach to construct sparse two class classifiers in terms of performance and computational efficiency.

Backward elimination model construction for regression and classification using leave-one-out criteria

A fundamental principle in practical nonlinear data modeling is the parsimonious principle of constructing the minimal model that explains the training data well. Leave-one-out (LOO) cross validation is often used to estimate generalization errors by choosing amongst different network architectures (M. Stone, "Cross validatory choice and assessment of statistical predictions", J. R. Stast. Soc., Ser. B, 36, pp. 117-147, 1974). Based upon the minimization of LOO criteria of either the mean squares of LOO errors or the LOO misclassification rate respectively, we present two backward elimination algorithms as model post-processing procedures for regression and classification problems. The proposed backward elimination procedures exploit an orthogonalization procedure to enable the orthogonality between the subspace as spanned by the pruned model and the deleted regressor. Subsequently, it is shown that the LOO criteria used in both algorithms can be calculated via some analytic recursive formula, as derived in this contribution, without actually splitting the estimation data set so as to reduce computational expense. Compared to most other model construction methods, the proposed algorithms are advantageous in several aspects; (i) There are no tuning parameters to be optimized through an extra validation data set; (ii) The procedure is fully automatic without an additional stopping criteria; and (iii) The model structure selection is directly based on model generalization performance. The illustrative examples on regression and classification are used to demonstrate that the proposed algorithms are viable post-processing methods to prune a model to gain extra sparsity and improved generalization.

Validation of short tandem repeat analysis for the investigation of cases of disputed paternity

This study details validation of two separate multiplex STR systems for use in paternity investigations. These are the Second Generation Multiplex (SGM) developed by the UK Forensic Science Service and the PowerPlex 1 multiplex commercially available from Promega Inc. (Madison, WI, USA). These multiplexes contain 12 different STR systems (two are duplicated in the two systems). Population databases from Caucasian, Asian and Afro-Caribbean populations have been compiled for all loci. In all but two of the 36 STR/ethnic group combinations, no evidence was obtained to indicate inconsistency with Hardy-Weinberg (HW) proportions. Empirical and theoretical approaches have been taken to validate these systems for paternity testing. Samples from 121 cases of disputed paternity were analysed using established Single Locus Probe (SLP) tests currently in use, and also using the two multiplex STR systems. Results of all three test systems were compared and no non-conformities in the conclusions were observed, although four examples of apparent germ line mutations in the STR systems were identified. The data was analysed to give information on expected paternity indices and exclusion rates for these STR systems. The 12 systems combined comprise a highly discriminating test suitable for paternity testing. 99.96% of non-fathers are excluded from paternity on two or more STR systems. Where no exclusion is found, Paternity Index (PI) values of > 10,000 are expected in > 96% of cases.

The 'child of Babylon' and the problem of paternity in Medieval French Alexander romances

A study of the use of hybrid physical appearance both to signal and to explore the disputed paternity of Alexander the Great throughout its vernacular French tradition. The article compares the 'child of Babylon' portent and Alexander's son Alior in the twelfth-century French "Roman d'Alexandre" poem cycle, and a fifteenth-century prose adaptation of it.

A radial basis function network classifier to maximise leave-one-out mutual information

tWe develop an orthogonal forward selection (OFS) approach to construct radial basis function (RBF)network classifiers for two-class problems. Our approach integrates several concepts in probabilisticmodelling, including cross validation, mutual information and Bayesian hyperparameter fitting. At eachstage of the OFS procedure, one model term is selected by maximising the leave-one-out mutual infor-mation (LOOMI) between the classifier’s predicted class labels and the true class labels. We derive theformula of LOOMI within the OFS framework so that the LOOMI can be evaluated efficiently for modelterm selection. Furthermore, a Bayesian procedure of hyperparameter fitting is also integrated into theeach stage of the OFS to infer the l2-norm based local regularisation parameter from the data. Since eachforward stage is effectively fitting of a one-variable model, this task is very fast. The classifier construc-tion procedure is automatically terminated without the need of using additional stopping criterion toyield very sparse RBF classifiers with excellent classification generalisation performance, which is par-ticular useful for the noisy data sets with highly overlapping class distribution. A number of benchmarkexamples are employed to demonstrate the effectiveness of our proposed approach.

Do you really want to ask an underwriter how much money you should leave on the table?

This paper analyses whether the owners of companies seeking to list will leave less money on the table if underwriters are employed to price and market the issue. Our findings indicate that limited liability and Industrial company initial public offerings (IPOs) that have used underwriters have left
more money on the table than those not employing underwriters. Not only is there a direct cost in employing an underwriter but this study suggests there might also be an indirect cost. We also find that a positive forecast earnings per share yield may be useful in reducing the amount of money left on the table.