13 found
Order:
  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 (...)
    Download  
     
    Export citation  
     
    Bookmark   2 citations  
  2.  7
    Full Bayesian Significance Test Applied to Multivariate Normal Structure Models.Marcelo de Souza Lauretto, Carlos Alberto de Braganca Pereira, Julio Michael Stern & Shelemiahu Zacks - 2003 - Brazilian Journal of Probability and Statistics 17:147-168.
    Abstract: The Pull Bayesian Significance Test (FBST) for precise hy- potheses is applied to a Multivariate Normal Structure (MNS) model. In the FBST we compute the evidence against the precise hypothesis. This evi- dence is the probability of the Highest Relative Surprise Set (HRSS) tangent to the sub-manifold (of the parameter space) that defines the null hypothesis. The MNS model we present appears when testing equivalence conditions for genetic expression measurements, using micro-array technology.
    Download  
     
    Export citation  
     
    Bookmark   2 citations  
  3.  7
    Testing Significance in Bayesian Classifiers.Julio Michael Stern & Marcelo de Souza Lauretto - 2005 - Frontiers in Artificial Intelligence and Applications 132:34-41.
    The Fully Bayesian Significance Test (FBST) is a coherent Bayesian significance test for sharp hypotheses. This paper explores the FBST as a model selection tool for general mixture models, and gives some computational experiments for Multinomial-Dirichlet-Normal-Wishart models.
    Download  
     
    Export citation  
     
    Bookmark   1 citation  
  4.  26
    A Weibull Wearout Test: Full Bayesian Approach.Julio Michael Stern, Telba Zalkind Irony, Marcelo de Souza Lauretto & Carlos Alberto de Braganca Pereira - 2001 - Reliability and Engineering Statistics 5:287-300.
    The Full Bayesian Significance Test (FBST) for precise hypotheses is presented, with some applications relevant to reliability theory. The FBST is an alternative to significance tests or, equivalently, to p-ualue.s. In the FBST we compute the evidence of the precise hypothesis. This evidence is the probability of the complement of a credible set "tangent" to the sub-manifold (of the para,rreter space) that defines the null hypothesis. We use the FBST in an application requiring a quality control of used components, based (...)
    Download  
     
    Export citation  
     
    Bookmark  
  5.  16
    Combining Optimization and Randomization Approaches for the Design of Clinical Trials.Julio Michael Stern, Victor Fossaluza, Marcelo de Souza Lauretto & Carlos Alberto de Braganca Pereira - 2015 - Springer Proceedings in Mathematics and Statistics 118:173-184.
    t Intentional sampling methods are non-randomized procedures that select a group of individuals for a sample with the purpose of meeting specific prescribed criteria. In this paper we extend previous works related to intentional sampling, and address the problem of sequential allocation for clinical trials with few patients. Roughly speaking, patients are enrolled sequentially, according to the order in which they start the treatment at the clinic or hospital. The allocation problem consists in assigning each new patient to one, and (...)
    Download  
     
    Export citation  
     
    Bookmark  
  6.  39
    FBST for a Generalized Poisson Distribution.Julio Michael Stern, Paulo do Canto Hubert & Marcelo de Souza Lauretto - 2009 - AIP Conference Proceedings 1193:210-217.
    The Generalized Poisson Distribution (GPD) adds an extra parameter to the usual Poisson distribution. This parameter induces a loss of homogeneity in the stochastic processes modeled by the distribution. Thus, the generalized distribution becomes an useful model for counting processes where the occurrence of events is not homogeneous. This model creates the need for an inferential procedure, to test for the value of this extra parameter. The FBST (Full Bayesian Significance Test) is a Bayesian hypotheses test procedure, capable of providing (...)
    Download  
     
    Export citation  
     
    Bookmark  
  7.  14
    FBST for Mixture Model Selection.Julio Michael Stern & Marcelo de Souza Lauretto - 2005 - AIP Conference Proceedings 803:121-128.
    The Fully Bayesian Significance Test (FBST) is a coherent Bayesian significance test for sharp hypotheses. This paper proposes the FBST as a model selection tool for general mixture models, and compares its performance with Mclust, a model-based clustering software. The FBST robust performance strongly encourages further developments and investigations.
    Download  
     
    Export citation  
     
    Bookmark  
  8.  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).
    Download  
     
    Export citation  
     
    Bookmark  
  9.  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 (...)
    Download  
     
    Export citation  
     
    Bookmark  
  10.  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 (...)
    Download  
     
    Export citation  
     
    Bookmark  
  11.  14
    The Full Bayesian Significance Test for Mixture Models: Results in Gene Expression Clustering.Julio Michael Stern, Marcelo de Souza Lauretto & Carlos Alberto de Braganca Pereira - 2008 - Genetics and Molecular Research 7 (3):883-897.
    Gene clustering is a useful exploratory technique to group together genes with similar expression levels under distinct cell cycle phases or distinct conditions. It helps the biologist to identify potentially meaningful relationships between genes. In this study, we propose a clustering method based on multivariate normal mixture models, where the number of clusters is predicted via sequential hypothesis tests: at each step, the method considers a mixture model of m components (m = 2 in the first step) and tests if (...)
    Download  
     
    Export citation  
     
    Bookmark  
  12.  10
    The Problem of Separate Hypotheses Via Mixtures Models.Julio Michael Stern, Marcelo de Souza Lauretto, Silvio Rodrigues Faria & Carlos Alberto de Braganca Pereira - 2007 - AIP Conference Proceedings 954:268-275.
    This article describes the Full Bayesian Significance Test for the problem of separate hypotheses. Numerical experiments are performed for the Gompertz vs. Weibull life span test.
    Download  
     
    Export citation  
     
    Bookmark  
  13.  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 (...)
    Download  
     
    Export citation  
     
    Bookmark