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  1.  13
    A Straightforward Multiallelic Significance Test for the Hardy-Weinberg Equilibrium Law.Julio Michael Stern, Marcelo de Souza Lauretto, Fabio Nakano, Silvio Rodrigues Faria & Carlos Alberto de Braganca Pereira - 2009 - Genetics and Molecular Biology 32 (3):619-625.
    Much forensic inference based upon DNA evidence is made assuming Hardy-Weinberg Equilibrium (HWE) for the genetic loci being used. Several statistical tests to detect and measure deviation from HWE have been devised, and their limitations become more obvious when testing for deviation within multiallelic DNA loci. The most popular methods-Chi-square and Likelihood-ratio tests-are based on asymptotic results and cannot guarantee a good performance in the presence of low frequency genotypes. Since the parameter space dimension increases at a quadratic rate on (...)
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  2.  10
    Genuine Bayesian Multiallelic Significance Test for the Hardy-Weinberg Equilibrium Law.Julio Michael Stern, Carlos Alberto de Braganca Pereira, Fabio Nakano & Martin Ritter Whittle - 2006 - Genetics and Molecular Research 5 (4):619-631.
    Statistical tests that detect and measure deviation from the Hardy-Weinberg equilibrium (HWE) have been devised but are limited when testing for deviation at multiallelic DNA loci is attempted. Here we present the full Bayesian significance test (FBST) for the HWE. This test depends neither on asymptotic results nor on the number of possible alleles for the particular locus being evaluated. The FBST is based on the computation of an evidence index in favor of the HWE hypothesis. A great deal of (...)
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  3.  9
    Hierarchical Forecasting with Polynomial Nets.Julio Michael Stern, Fabio Nakano, Marcelo de Souza Lauretto & Carlos Alberto de Braganca Pereira - 2009 - Studies in Computational Intelligence 199:305-315.
    This article presents a two level hierarchical forecasting model developed in a consulting project for a Brazilian magazine publishing company. The first level uses a VARMA model and considers econometric variables. The second level takes into account qualitative aspects of each publication issue, and is based on polynomial networks generated by Genetic Programming (GP).
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  4.  16
    Intentional Sampling by Goal Optimization with Decoupling by Stochastic Perturbation.Julio Michael Stern, Marcelo de Souza Lauretto, Fabio Nakano & Carlos Alberto de Braganca Pereira - 2012 - AIP Conference Proceedings 1490:189-201.
    Intentional sampling methods are non-probabilistic procedures that select a group of individuals for a sample with the purpose of meeting specific prescribed criteria. Intentional sampling methods are intended for exploratory research or pilot studies where tight budget constraints preclude the use of traditional randomized representative sampling. The possibility of subsequently generalize statistically from such deterministic samples to the general population has been the issue of long standing arguments and debates. Nevertheless, the intentional sampling techniques developed in this paper explore pragmatic (...)
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  5.  7
    Real Attribute Learning Algorithm.Julio Michael Stern, Marcelo de Souza Lauretto, Fabio Nakano & Celma de Oliveira Ribeiro - 1998 - ISAS-SCI’98 2:315-321.
    This paper presents REAL, a Real-Valued Attribute Classification Tree Learning Algorithm. Several of the algorithm's unique features are explained by úe users' demands for a decision support tool to be used for evaluating financial operations strategies. Compared to competing algorithms, in our applications, REAL presents maj or advantages : (1) The REAL classification trees usually have smaller error rates. (2) A single conviction (or trust) measure at each leaf is more convenient than the traditional (probability, confidence-level) pair. (3) No need (...)
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  6.  17
    TORC3: Token-Ring Clearing Heuristic for Currency Circulation.Julio Michael Stern, Carlos Humes, Marcelo de Souza Lauretto, Fabio Nakano, Carlos Alberto de Braganca Pereira & Guilherme Frederico Gazineu Rafare - 2012 - AIP Conference Proceedings 1490:179-188.
    Clearing algorithms are at the core of modern payment systems, facilitating the settling of multilateral credit messages with (near) minimum transfers of currency. Traditional clearing procedures use batch processing based on MILP - mixed-integer linear programming algorithms. The MILP approach demands intensive computational resources; moreover, it is also vulnerable to operational risks generated by possible defaults during the inter-batch period. This paper presents TORC3 - the Token-Ring Clearing Algorithm for Currency Circulation. In contrast to the MILP approach, TORC3 is a (...)
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